The Utilization of Patient-Reported Outcome Measures in Assessing the Treatment of Osteochondral Lesions of the Ankle Versus the Knee
Bibliographic record
Abstract
Background: When assessing the outcomes of ankle and knee osteochondral lesions (OCLs), there are numerous patient-reported outcome measures (PROMs) that are used; however, not all are validated. Purpose: To compare the utilization of PROMs in assessing the treatment of ankle OCLs versus knee OCLs. Study Design: Systematic review; Level of evidence, 4. Methods: A systematic search of Embase, MEDLINE, and CINAHL was conducted to identify all observational or experimental studies from January 1, 2014 to December 31, 2023 that used PROMs to assess the treatment of ankle or knee OCLs. The frequency of the use of specific validated PROMs between the ankle OCL and knee OCL literature was compared using an independent t -test. Correlation coefficients were calculated to assess differences based on journal impact factor (divided into quartiles), publication year, or level of evidence. Results: A total of 233 eligible ankle OCL studies and 211 knee OCL studies were identified. Validated clinical outcome measures were used in 41.2% of ankle OCL studies compared with 87.7% of knee OCL studies ( P < .001). There were a total of 44 outcome measures used in ankle OCL studies, with the majority of studies (67.8%) utilizing the AOFAS (American Orthopaedic Foot and Ankle Society) score. There were no correlations between the use of validated outcome measures in the ankle OCL studies and journal impact factor ( P = .78), publication year ( P = .16), or level of evidence ( P = .45). Similarly, there were no correlations for the knee OCL studies based on journal impact factor ( P = .60), publication year ( P = .25), or level of evidence ( P = .55). Conclusion: Validated clinical outcome measures were more frequently utilized in knee OCL studies compared with ankle OCL studies. The low frequency of validated outcome measures used within the ankle literature may limit how well treatment effectiveness in the management of ankle OCLs is evaluated.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.087 | 0.227 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".