The impact of surgical randomised controlled trials on the management of FAI syndrome: a citation analysis
Bibliographic record
Abstract
PURPOSE: To identify and assess the clinical impact of randomised controlled trials (RCTs) assessing the surgical management of femoroacetabular impingement syndrome (FAIS) through a citation analysis. METHODS: MEDLINE, EMBASE and CENTRAL were searched from inception to April 22, 2023 for RCTs assessing the surgical management of FAIS. Study characteristics were directly abstracted from included trials and citation metrics were obtained from the Clarivate Web of Knowledge database on May 19, 2023. The continuous fragility index (CFI) was calculated for eligible outcomes. Univariate regression models were used to explore correlations between total citations per year and various study characteristics. RESULTS: Ten studies comprising one thousand two hundred ninetypatients were eligible for analysis. Studies were published from 2013 to 2023. Eight countries were represented across various trials with 91% being either North American or European. The mean journal impact factor of published studies was 39.684 (median 2.982; range 1.31-202.73). The mean citation density was 14.17 (range 0.33-48.67). The median CFI was 4.8 (range 1-32.2). Correlation analysis demonstrated strong and statistically significant correlations to study sample size (R = 0.75, p = 0.012), journal impact factor (R = 0.80, p = 0.006) and continuous fragility index (R = 0.95, p = 0.015). CONCLUSION: Trials assessing the surgical management of FAIS present with a wide range of clinical uptake based on citation density and are published in journals of broadly variable impact factor. Despite promising citation metrics, high-quality evidence on arthroscopy for FAIS is limited to the United States and Europe with an unclear international impact. Future knowledge translation efforts are warranted to maximise the international uptake of evidence regarding arthroscopic management of FAIS. LEVEL OF EVIDENCE: I.
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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 | 0.255 | 0.669 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.030 |
| Bibliometrics | 0.056 | 0.045 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".