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Introduction Establishing thresholds of change that are actually meaningful for the patient in an outcome measurement instrument is paramount. This concept is called the minimum clinically important difference (MCID). We summarize available MCID calculation methods relevant to spine surgery, and outline key considerations, followed by a step-by-step working example of how MCID can be calculated, using publicly available data, to enable the readers to follow the calculations themselves. Methods Thirteen MCID calculations methods were summarized, including anchor-based methods, distribution-based methods, Reliable Change Index, 30% Reduction from Baseline, Social Comparison Approach and the Delphi method. All methods, except the latter two, were used to calculate MCID for improvement of Zurich Claudication Questionnaire (ZCQ) Symptom Severity of patients with lumbar spinal stenosis. Numeric Rating Scale for Leg Pain and Japanese Orthopaedic Association Back Pain Evaluation Questionnaire Walking Ability domain were used as anchors. Results The MCID for improvement of ZCQ Symptom Severity ranged from 0.8 to 5.1. On average, distribution-based methods yielded lower MCID values, than anchor-based methods. The percentage of patients who achieved the calculated MCID threshold ranged from 9.5% to 61.9%. Conclusions MCID calculations are encouraged in spinal research to evaluate treatment success. Anchor-based methods, relying on scales assessing patient preferences, continue to be the “gold-standard” with receiver operating characteristic curve approach being optimal. In their absence, the minimum detectable change approach is acceptable. The provided explanation and step-by-step example of MCID calculations with statistical code and publicly available data can act as guidance in planning future MCID calculation studies.

Background Positive results for clinical outcomes should be not only statistically significant, but also clinically significant. The minimum clinically important difference (MCID) is used to define the minimum threshold of clinical significance. The anchor-based method is a classical method for ascertaining MCID. This study aimed to summarise the design of the anchors of the anchor-based method by reviewing the existing research and providing references and suggestions. Method This study was mainly based on literature research. We performed a systematic search using Web of Science, PubMed, CNKI, Wanfang, and VIP databases. Two reviewers independently screened titles and abstracts to identify relevant articles. Data were extracted from eligible articles using a predefined data collection form. Discrepancies were resolved by discussion and the involvement of a third reviewer. Result Three hundred and forty articles were retained for final analysis. For the design of anchors, Subjective anchors (99.12%) were the most common type of anchor used, mainly the Patient’s rating of change or patient satisfaction (66.47%) and related scale health status evaluation items or scores (39.41%). Almost half of the studies (48.53%) did not assess the correlation test between the anchor and the research indicator or scale. The cut-off values and grouping were usually based on the choice of the anchor types. In addition, due to the large number of included studies, this study selected the most calculated SF-36 (28 articles) for an in-depth analysis. The results showed that the overall design of the anchor and the cut-off value were the same as above. The statistical methods used were mostly traditional (mean change, ROC). The MCID thresholds of these studies had a wide range (SF-36 PCS: 2–17.4, SF-36 MCS: 1.46–10.28), and different anchors or statistical methods lead to different results. Conclusion It is of great importance to select several types of anchors and to use more reliable statistical methods to calculate the MCID. It is suggested that the order of selection of anchors should be: objective anchors > anchors with established MCID in subjective anchors (specific scale > generic scale) > ranked anchors in subjective anchors. The selection of internal anchors should be avoided, and anchors should be evaluated by a correlation test.

Lane detection plays a pivotal role in the successful implementation of Advanced Driver Assistance Systems (ADASs), which are essential for detecting the road’s lane markings and determining the vehicle’s position, thereby influencing subsequent decision making. However, current deep learning-based lane detection methods encounter challenges. Firstly, the on-board hardware limitations necessitate an exceptionally fast prediction speed for the lane detection method. Secondly, improvements are required for effective lane detection in complex scenarios. This paper addresses these issues by enhancing the row-anchor-based lane detection method. The Transformer encoder–decoder structure is leveraged as the row classification enhances the model’s capability to extract global features and detect lane lines in intricate environments. The Feature-aligned Pyramid Network (FaPN) structure serves as an auxiliary branch, complemented by a novel structural loss with expectation loss, further refining the method’s accuracy. The experimental results demonstrate our method’s commendable accuracy and real-time performance, achieving a rapid prediction speed of 129 FPS (the single prediction time of the model on RTX3080 is 15.72 ms) and a 96.16% accuracy on the Tusimple dataset—a 3.32% improvement compared to the baseline method.

The overarching goals of treatments for orthopedic conditions are generally to improve or restore function and alleviate pain. Results of clinical trials are generally used to determine whether a treatment is efficacious; however, a statistically significant improvement may not actually be clinically important, i.e., meaningful to the patient. To determine whether an intervention has produced clinically important benefits requires a two-step process: first, determining the magnitude of change considered clinically important for a particular measure in the relevant population and, second, applying this yardstick to a patient’s data to determine whether s/he has benefited from treatment. Several metrics have been devised to quantify clinically important differences, including the minimum clinically important difference (MCID) and clinically important difference (CID). Herein, we review the methods to generate the MCID and other metrics and their use and interpretation in clinical trials and practice. We particularly highlight the many pitfalls associated with the generation and utilization of these metrics that can impair their correct use. These pitfalls include the fact that different pain measures yield different MCIDs, that efficacy in clinical trials is impacted by various factors (population characteristics, trial design), that the MCID value is impacted by the method used to calculate it (anchor, distribution), by the type of anchor chosen and by the definition (threshold) of improvement. The MCID is also dependent on the population characteristics such as disease type and severity, sex, age, etc. For appropriate use, the MCID should be applied to changes in individual subjects, not to group changes. The MCID and CID are useful tools to define general guidelines to determine whether a treatment produces clinically meaningful effects. However, the many pitfalls associated with these metrics require a detailed understanding of the methods to calculate them and their context of use. Orthopedic surgeons that will use these metrics need to carefully understand them and be aware of their pitfalls.

This study aims to describe the distribution of anchor-based minimal important change (MIC) estimates in standard deviation (SD) units and examine if the robustness of such estimates depends on the specific SD used or on the methodological credibility of the anchor-based estimates. We included all anchor-based MIC estimates from studies published in MEDLINE and relevant literature databases upto October 2018. Each MIC was converted to SD units using baseline, endpoint, and change from baseline SDs. We performed a descriptive analysis of MICs in SD units and checked how the distribution would change if MICs with low methodological credibility were excluded from the analysis. We included 1,009 MIC estimates from 182 studies. The medians and interquartile ranges of MICs in SD units were 0.43 (0.25 to 0.69), 0.42 (0.22 to 0.70), and 0.51 (0.28 to 0.78) for baseline, endpoint, and change SD units, respectively. Some MICs were extremely large or small. The distribution did not change significantly after excluding MICs estimated by less credible methods. The size of the universally applicable MIC in SD units could not be determined. Anchor-based MICs in SD units were widely distributed, with more than half in the range of 0.2 to 0.8.

The objective of this review is to summarize recommendations on methods for evaluating responsiveness and minimal important difference (MID) for patient-reported outcome (PRO) measures. We review, summarize, and integrate information on issues and methods for evaluating responsiveness and determining MID estimates for PRO measures. Recommendations are made on best-practice methods for evaluating responsiveness and MID. The MID for a PRO instrument is not an immutable characteristic, but may vary by population and context, and no one MID may be valid for all study applications. MID estimates should be based on multiple approaches and triangulation of methods. Anchor-based methods applying various relevant patient-rated, clinician-rated, and disease-specific variables provide primary and meaningful estimates of an instrument's MID. Results for the PRO measures from clinical trials can also provide insight into observed effects based on treatment comparisons and should be used to help determine MID. Distribution-based methods can support estimates from anchor-based approaches and can be used in situations where anchor-based estimates are unavailable. We recommend that the MID is based primarily on relevant patient-based and clinical anchors, with clinical trial experience used to further inform understanding of MID.

Purpose Calculating the threshold for meaningful within-individual change (MWIC) is essential for interpreting patient-reported outcomes (PRO). However, traditional methods of determining MWIC threshold yield varying estimates and lack a standardized approach. We aim to propose a novel method for more accurate MWIC threshold estimation. Methods We developed a weighted predictive modeling method. The weighting involved using the rank difference between PRO score change and the anchor of each individual. A Monte Carlo simulation was conducted to compare the performance of the new method and that of existing state-of-the-art methods. Simulation parameters included distributions of PRO score changes, sample sizes, improvement proportions, and correlation strengths. Statistical performance was assessed using relative bias (rbias), coefficient of variation (CV), and relative root mean squared error (rRMSE). Results Distribution-based methods had the largest rbias and rRMSE among all methods. Existing anchor-based methods except for the Terluin 2022 method were biased when the correlation strength was weak or when the improvement proportion was not 50%. The Terluin 2022 method requires estimating an important reliability parameter, and this method had highest CV compared to other predictive modeling methods. The new weighted method demonstrated the smallest rRMSE across most simulation settings. It also maintained relatively high accuracy under weak correlation strength or imbalanced improvement proportion. Similar results were presented under normal or skewed distributions of PRO score changes. Conclusion This novel method offers a simple and feasible alternative to existing predictive modeling methods for estimating MWIC threshold, which can facilitate the application of PRO.

To evaluate reporting of minimal important difference (MID) estimates using anchor-based methods for patient-reported outcome measures (PROMs), and the association with reporting deficiencies on their credibility. Systematic survey of primary studies empirically estimating MIDs. We searched Medline, EMBASE, PsycINFO, and the Patient-Reported Outcome and Quality of Life Instruments Database until October 2018. We evaluated study reporting, focusing on participants’ demographics, intervention(s), characteristics of PROMs and anchors, and MID estimation method(s). We assessed the impact of reporting issues on credibility of MID estimates. In 585 studies reporting on 5,324 MID estimates for 526 distinct PROMs, authors frequently failed to adequately report key characteristics of PROMs and MIDs, including minimum and maximum values of PROM scale, measure of variability accompanying the MID estimate and number of participants included in the MID calculation. Across MID estimates (n = 5,324), the most serious reporting issues impacting credibility included infrequent reporting of the correlation between the anchor and PROM (66%), inadequate details to judge precision of MID point estimate (13%), and insufficient information about the threshold used to ascertain MIDs (16%). Serious issues of incomplete reporting in the MID literature threaten the optimal use of MID estimates to inform the magnitude of effects of interventions on PROMs.

This study aimed to estimate patient acceptable symptom state (PASS) thresholds in the Forgotten Joint Score (FJS) 1 year following primary total knee arthroplasty (PTKA) while investigating the impact of patients’ characteristics on PASS thresholds. This cohort study used data from patients who underwent PTKA at a public hospital in Scotland between April 2021 and December 2022. Assessment of FJS (0-100, high-low knee awareness) was completed 1 year postoperatively. A single-item question about satisfaction with the operated knee was completed at 1 year and served as the anchor for estimating PASS thresholds. Anchor-based predictive modeling (adjusted and unadjusted) and receiver operating characteristic (ROC) curve methods were used to determine PASS thresholds. The impact of patient characteristics on PASS threshold values was investigated by calculating stratified PASS values based on gender and age groups. A total of 1832 PTKAs were performed between April 2021 and December 2022, of which 1359 (74%) had complete data comprising the study cohort. The median age and body mass index of patients included in the study were 70 years and 31.2 kg/m2, respectively, with 54% being females. The proportion of satisfied patients was 84%. A moderate positive correlation between FJS and patient satisfaction was found (r = 0.64, P < .001), which supports the validity of the external anchor. PASS thresholds for the entire cohort were 31 (ROC method) and ∼33 points (predictive modeling method). Larger PASS values were found for male patients and patients aged ≥70 years compared to their female and younger counterparts. The adjusted predictive modeling estimate was 15.3; given that the data do not meet the assumption of normal distribution, we consider this threshold might be biased and must be interpreted with circumspection. A postoperative FJS of ≥33 points can be used as a reference guide to evaluate successful achievement of a “forgotten joint” in a Scottish population. Patients’ characteristics impact PASS estimates and should be considered when interpreting outcome scores. After knee replacement surgery, patients want their new knee to feel natural. The FJS measures how much a person notices their artificial knee in daily life. The score goes from 0 to 100, with higher numbers meaning the knee feels more natural. We studied data from over 1300 patients in Scotland using modern statistical methods. Our results show that most patients need a score of at least 33 to feel satisfied with their knee 1 year after surgery, while men and people aged ≥70 years seem to need higher scores. [Display omitted] •Men and patients aged 70+ years have higher patient acceptable symptom state values.•Adjusted predictive modeling (APM) method decreased the thresholds by more than 50%.W•New PASS thresholds for the FJS 1 year after TKA based on a large Scottish cohort.•Validity of APM method in skewed data needs robust investigation and establishment.•APM studies should report unadjusted values and data properties to enhance result interpretation.