Peri-Operative Management of Periacetabular Osteotomy: A Report of Current Practices from the Anchor Group, Supporting Literature, and Areas for Future Investigation.
Bibliographic record
Abstract
Background: Periacetabular osteotomy (PAO) is a well-established surgical treatment for hip dysplasia, but very few studies report the impact of peri-operative management strategies on early pain and function. The purpose of this study is to describe peri-operative management variability among a group of experienced surgeons and review the literature supporting these practice patterns. Methods: We surveyed 16 surgeons that perform PAO to document various aspects of peri-operative management at four stages: pre-operative, intra-operative, post-operative in the hospital, and at discharge. Our goal was to report current surgical pain management strategies, adjunct medications, type of anesthesia, deep venous thrombosis and heterotopic ossification prophylaxis strategies, initiation of physical therapy, and use of continuous passive motion (CPM). We reviewed current literature to identify studies supporting these perioperative strategies and identify knowledge gaps that would benefit from further investigation. Results: Of the 16 surgeons surveyed, 75% had been in practice greater than 10 years and most had not altered their post-operative protocol for more than 3 years. 15/16 surgeons felt that length of stay could be reduced at their institution with improved peri-operative pain management. 6/16 were considering or had already implemented outpatient PAO as a part of their practice. We found significant variability in the pain medications provided at all peri-operative stages. 14/16 utilized general anesthesia, and many utilized epidural or peripheral nerve blocks. 6/16 surgeons utilized surgical field block (also referred to as periarticular block). These surgeons advocated that surgical field block was an effective intervention with no/minimal complication risk. There is very little literature critically evaluating efficacy of these perioperative management strategies for PAO. Conclusion: .
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".