The Learning Curve for Hip Arthroscopy: A Systematic Review
Bibliographic record
Abstract
PURPOSE: The learning curve for hip arthroscopy is consistently characterized as "steep." The purpose of this systematic review was to (1) identify the various learning curves reported in the literature, (2) examine the evidence supporting these curves, and (3) determine whether this evidence supports an accepted number of cases needed to achieve proficiency. METHODS: The electronic databases Embase and Medline were screened for any clinical studies reporting learning curves in hip arthroscopy. Two reviewers conducted a full-text review of eligible studies and a hand search of conference proceedings and reference sections of the included articles. Inclusion/exclusion criteria were applied, and a quality assessment was completed for each included article. Descriptive statistics were compiled. RESULTS: We identified 6 studies with a total of 1,063 patients. Studies grouped surgical cases into "early" versus "late" in a surgeon's experience, with 30 cases being the most common cutoff used. Most of these studies used descriptive statistics and operative time and complication rates as measures of competence. Five of 6 studies showed improvement in these measures between early and late experience, but only one study proposed a bona fide curve. CONCLUSIONS: This review shows that when 30 cases was used as the cutoff point to differentiate between early and late cases in a surgeon's experience, there were significant reductions in operative time and complication rates. However, there was insufficient evidence to quantify the learning curve and validate 30, or any number of cases, as the point at which the learning curve plateaus. As a result, this number should be interpreted with caution. LEVEL OF EVIDENCE: Level IV, systematic review of Level IV studies.
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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 | 0.016 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".