Postoperative Pain After Surgical Treatment of Ankle Fractures: A Prospective Study
Bibliographic record
Abstract
BACKGROUND: Postoperative pain after fixation of ankle fractures has a substantial effect on surgical outcome and patient satisfaction. Patients requiring large amounts of narcotics are at higher risk of long-term use of pain medications. Few prospective studies investigate patient pain experience in the management of ankle fractures. METHODS: We prospectively evaluated the pain experience in 63 patients undergoing open reduction and internal fixation of ankle. The Short-Form McGill Pain Questionnaire was administered preoperatively and postoperatively (PP) at 3 days (3dPP) and 6 weeks (6wPP). Anticipated postoperative pain (APP) was recorded. RESULTS: No significant differences were found between PP, APP, and 3dPP; however, 6wPP was markedly lower. Significant correlations were found between PP and APP and between preoperative and postoperative Short-Form McGill Pain Questionnaire scores. PP and APP were independent predictors of 3dPP; however, only APP was predictive of 6wPP. Sex, age, and inpatient versus outpatient status were not notable factors. No statistically significant differences were found in pain scores between fracture types. CONCLUSIONS: Both preoperative pain severity and anticipated postoperative pain are predictive of postoperative pain levels. Orthopaedic surgeons should place a greater focus on the postoperative management of patient pain and expectations after surgical procedures.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".