Results of the PeRception of femOroaCetabular impingEment by Surgeons Survey (PROCESS)
Bibliographic record
Abstract
PURPOSE: Currently, there is a lack of high-level evidence addressing the variety of treatment options available for patients diagnosed with femoroacetabular impingement (FAI). The objective was to determine the current state of practice for FAI in Canada. METHODS: A questionnaire was developed and pretested to address the current state of knowledge among orthopaedic surgeons regarding FAI treatment using a focus group of experts, reviewing prior surveys, and reviewing online guidelines addressing surgical interventions for FAI. The membership of the Canadian Orthopaedic Association (COA) was surveyed through email and mail in both French and English. RESULTS: Two hundred and two surveys were obtained (20 % response rate), of which 74.3 % of respondents manage patients under age 40 with hip pain. Most surgeons (62 %) considered failure of non-operative management as the most important indication for the surgical management of FAI, usually by treating both bony and soft tissue damage (54.4 %). The majority of surgeons were unsure of the existence of evidence supporting the best clinical test for FAI, the use of a diagnostic intra-articular injection for diagnosis of FAI, and for non-operative management of FAI. One in four respondents supported a sham surgery (24.8 %) control arm for a trial evaluating the impact of surgical intervention on FAI. CONCLUSIONS: This survey elucidates areas of research for future studies relevant to FAI and highlights controversial areas of treatment. The results suggest that the current management of FAI by members of the COA is limited by a lack of awareness of high-level evidence.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".