Incidence, Risk Factors, and Diagnostic Evaluation of Postoperative Fever in an Orthopaedic Trauma Population
Bibliographic record
Abstract
OBJECTIVES: To determine the incidence of positive diagnostic evaluations during the management of postoperatively febrile orthopaedic trauma patients. A secondary objective was to describe the incidence and risk factors for postoperative fever. DESIGN: Retrospective study. SETTING: The orthopaedic trauma service at a tertiary referral hospital. PATIENTS: Postoperatively febrile orthopaedic trauma patients admitted from 2005 to 2008. MAIN OUTCOME MEASURES: In patients who developed postoperative fever (oral temperature ≥38.5ºC), records were reviewed to determine whether urinalysis, urine cultures, blood cultures, chest radiographs, or wound cultures were performed and subsequent results were recorded. Patient demographics including sex, age, and medical comorbidities were also noted. RESULTS: A total of 106 subjects (18%) developed a postoperative fever, with a mean temperature of 38.8 ± 0.3ºC (range, 38.5-40.0ºC). Overall, 135 diagnostic tests were ordered with 14 being positive (10%). Yields per individual test were as follows: urinalyses, 7 of 34 (21%); urine cultures, 4 of 38 (11%); chest radiograph, 2 of 23 (9%); blood cultures, 1 of 38 (3%); and wound cultures, 0 of 2 (0%). Patients investigated on postoperative day 6 or later had a greater incidence of positive diagnostic evaluations (40%) than patients investigated on postoperative days 0-5 (16%). The single positive blood culture was found on postoperative day 16. CONCLUSIONS: Postoperative fever is common among orthopaedic trauma patients. Diagnostic evaluations have a low-positive yield, particularly in the early postoperative period. In the later postoperative period, physicians should be more suspicious for an infective source of fever because a traumatic inflammatory etiology of fever is less likely.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".