Predictors of Reoperation Following Operative Management of Fractures of the Tibial Shaft
Bibliographic record
Abstract
BACKGROUND: Accurate prediction of likelihood of reoperation in patients with tibial shaft fractures would facilitate optimal management. Previous studies were limited by small sample sizes and noncomprehensive examination of possible risk factors. OBJECTIVE: We conducted an observational study to determine which prognostic factors were associated with an increased risk of reoperation following operative treatment in a heterogeneous population of patients with tibial shaft fractures. DESIGN: Retrospective observational study. SETTING: Level 1 trauma center. METHODS: We identified 200 patients with tibial shaft fractures from two university-affiliated centers. Two reviewers independently abstracted data regarding 20 possible prognostic variables, reviewed preoperative and postoperative radiographs, and documented reoperations (defined as any surgical procedure </=1 year after the initial surgery that was aimed specifically at achieving bony union of the fracture, including bone grafts, implant exchanges, or débridement for infections). We chose a Cox proportion hazards model to conduct a survival analysis for time to reoperation and constructed a multivariable model to estimate the relative risk of reoperation and associated 95%confidence interval (CI) for each predictor variable. MAIN OUTCOME MEASURES: Time to reoperation following the initial surgery. RESULTS: Complete follow-up information was available for 192 of 200 (96%) patients. Three variables predicted reoperation: the presence of an open fracture wound (relative risk 4.32, 95% CI 1.76 to 11.26), lack of cortical continuity between the fracture ends following fixation (relative risk 8.33, 95% CI 3.03 to 25.0), and the presence of a transverse fracture (relative risk 20.0, 95% CI 4.34 to 142.86). CONCLUSIONS: We identified a set of three simple prognostic variables (open fracture, transverse fracture, and postoperative fracture gap) that can assist surgeons in predicting reoperation following operative treatment of tibial shaft fractures.
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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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".