Pain management practice patterns after hip arthroscopy: an international survey
Bibliographic record
Abstract
Several post-operative pain control methods have been described for hip arthroscopy including systemic medications, intra-articular or peri-portal injection of local anesthetics and peripheral nerve blocks. The diversity of modalities used may reflect a lack of consensus regarding an optimal approach. The purpose of this investigation was to conduct an international survey to assess pain management patterns after hip arthroscopy. It was hypothesized that a lack of agreement would be present in the majority of the surgeons' responses. A 25-question multiple-choice survey was designed and distributed to members of multiple orthopedic professional organizations related to sports medicine and hip arthroscopy. Clinical agreement was defined as > 80% of respondents selecting a single answer choice, while general agreement was defined as >60% of a given answer choice. Two hundred and fifteen surgeons completed the survey. Clinical agreement was only evident in the use of oral non-steroidal anti-inflammatory drugs (NSAIDs) for pain management after hip arthroscopy. A significant number of respondents (15.8%) had to readmit a patient to the hospital for pain control in the first 30 days after hip arthroscopy in the past year. There is significant variability in pain management practice after hip arthroscopy. The use of oral NSAIDs in the post-operative period was the only practice that reached a clinical agreement. As the field of hip preservation surgery continues to evolve and expand rapidly, further research on pain management after hip arthroscopy is clearly needed to establish evidence-based guidelines and improve clinical practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".