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Background: Despite focus on surgical preservation of the chondrolabral junction (CLJ), the transition zone between the acetabular cartilage and labrum, the association between severity of CLJ breakdown and functional outcomes after hip arthroscopy remains unexplored. Purpose: To assess the influence of CLJ breakdown on patient-reported outcome measures (PROMs) at a 24-month follow-up after hip arthroscopy for symptomatic labral tears. Study Design: Cohort study; Level of evidence, 3. Methods: A retrospective review of prospectively collected data was conducted to identify patients ≥18 years of age with a minimum 24-month follow-up who underwent hip arthroscopy by a single surgeon for the treatment of symptomatic labral tears secondary to femoroacetabular impingement. The Beck classification of transition zone cartilage was used to grade CLJ damage; patients with grades 0 to 2 were stratified into the mild CLJ damage cohort, and those with grades 3 and 4 were stratified into the severe CLJ damage cohort. PROMs were collected at baseline and at 3, 6, 12 months, and annually thereafter postoperatively. Linear mixed-effects models were used to compare PROMs. Rates of achieving clinically meaningful thresholds and subsequent surgery rates were also compared. Results: In total, 198 patients met the inclusion criteria, with a mean follow-up of 3.54 ± 1.26 years. A total of 95 patients with severe CLJ damage (mean age, 34.9 ± 10.5 years) were compared with 103 patients with mild CLJ damage (mean age, 38.2 ± 11.9 years). Hip Outcome Score–Activities of Daily Living (HOS-ADL), Non-Arthritic Hip Score (NAHS), and visual analog score for pain were inferior in the severe CLJ group at enrollment and all follow-up time points (P ≤.05). However, patients with severe CLJ breakdown exhibited greater improvements in HOS-ADL and NAHS at the 24-month follow-up and achieved clinically meaningful thresholds at equivalent rates to patients with mild CLJ breakdown. Subsequent surgery rates were 6.8% and 12.6% in patients with mild versus severe CLJ damage, respectively (P =.250). Conclusion: Severe CLJ breakdown is associated with increased pain and decreased functional level preoperatively and up to 24 months after hip arthroscopy. Despite this, patients with severe CLJ breakdown experienced greater improvements in functional outcomes at a 24-month follow-up and achieved clinical thresholds at similar rates to patients with mild CLJ damage. Thus, while worse baseline pain and functional levels may indicate severe CLJ breakdown, these patients still benefit substantially from hip arthroscopy. [ABSTRACT FROM AUTHOR]

The Fragility Index (FI) is defined as the number of patients whose outcome would need to change to reverse a statistically significant finding to a nonsignificant finding. The FI is nothing more than a repackaging of statistical significance based on the P value, perpetuating (1) ignoring of results that are "not" statistically significant; (2) treating results that are statistically significant as certain; and (3) distracting from evaluation of clinical significance. A well-designed trial includes a sample size calculation to determine the minimum number of patients required to observe a difference between study groups (if a difference exists). By including this minimum number, clinical trials are designed to be fragile, yet subsequently criticized as such, leading readers to the incorrect conclusion that the studies are flawed. It's time to move past systematic reviews focused on the FI. [ABSTRACT FROM AUTHOR]

Essential elements required for proper use of artificial intelligence machine learning tools in biomedical research and scientific publications include (1) explanation justifying why a machine learning approach contributes to the purpose of the study; (2) description of the adequacy of the data (input) to produce the desired results (output); (3) details of the algorithmic (i.e., computational) approach including methods for organizing the data (preprocessing); the machine learning computational algorithm(s) assessed; on what data the models were trained; the presence of bias and efforts to mitigate these effects; and the methods for quantifying the variables (features) most influential in determining the results (e.g., Shapley values); (4) description of methods, and reporting of results, quantitating performance in terms of both model accuracy and model calibration (level of confidence in the model's predictions); (5) availability of the programming code (including a link to the code when available—ideally, the code should be available); (6) discussion of model internal validation (results applicable and sensitive to the population investigated and data on which the model was trained) and external validation (were the results investigated as to whether they are generalizable to different populations? If not, consideration of this limitation and discussion of plans for external validation, i.e., next steps). As biomedical research submissions using artificial intelligence technology increase, these requirements could facilitate purposeful use and comprehensive methodological reporting. [ABSTRACT FROM AUTHOR]

The minimal clinically important difference (MCID) is a frequently reported metric for describing within-patient improvement in patient-reported outcome measures (PROMs). It was originally defined by Jaeschke et al. as "the smallest difference in score in the domain of interest which patients perceive as beneficial and which would mandate, in the absence of troublesome side effects and excessive cost, a change in the patient's management." The latter part of this statement is often omitted, and this results in a loss of the originally intended value through lack of sufficient clinical importance to change management. Other pitfalls in the use of the MCID include that they are population- and condition-specific. As such, MCIDs lack external validity and cannot easily be extrapolated from one study to another. Furthermore, broadly different values can be obtained depending on the calculation method used. This makes the MCID an unhelpful metric when seeking to understand the true efficacy of a given intervention. The Food and Drug Administration recommends anchor-based methodologies (which take into account patient perception), over distribution-based methods (which are purely statistical and do not account for clinical meaningfulness to patients). Regardless, it should be noted that even anchor-based methodologies are susceptible to statistical bias, and measures are apt to be influenced by the regression to mean phenomena, where the value of the preintervention scores and their relationship to postintervention scores can bias estimates of the MCID. Finally, when using MCIDs, one must consider that they are a low bar. This means that patients do not undergo treatment to achieve minimally perceptible clinical improvements; instead, they undergo treatment with the hope of achieving substantial clinical benefit or a patient acceptable symptom state, and so these are more appropriate individual-level metrics to consider when evaluating clinically meaningful outcomes of treatment. [ABSTRACT FROM AUTHOR]

Disaggregation, in the medical literature, means separation into demographic groups. This results in an opportunity to discover differences in outcomes by group, which could improve future treatments and provide outcome data, by group, that could be included in future systematic reviews. In research, the term disaggregation is most often used in reference to addressing inequities. We support the Sex and Gender Equity Research (SAGER) guidelines and encourage authors to examine how sex and gender are taken into account in their study and ensure adequate representation by sex and gender. (We respect that not all studies can or are designed to capture data by sex and gender, and that gender is "complex" and "fluid.") Disaggregation is encouraged, when possible, for other demographic variables allowing evaluation of all marginalized (as well as nonmarginalized) populations, so that we can better care for patients. [ABSTRACT FROM AUTHOR]

The mathematical formulas underpinning biostatistical modeling in medical research can be complicated. Nomograms are pictoral tools showing a graphical solution of a formula in which all variables in the formula can be read off the diagram. Nomograms can be used to simplify the calculation of risk of recurrence of injuries or disease. Although nomograms can be valuable, external validation is required (of all models) to ensure accuracy outside of the original population, because variation in patient populations, activities, and risk behaviors outside of the patient data used in the original analysis may deteriorate model performance (external validity). A model also needs to be maintained over time to ensure continued accuracy as populations and activities change. A model that fails to keep up will eventually have its accuracy and clinical utility fade. An externally validated, well-maintained model can be a valuable tool for patient counseling and decision-making. [ABSTRACT FROM AUTHOR]