Timing of hip hemiarthroplasty and the influence on prosthetic joint infection
Bibliographic record
Abstract
INTRODUCTION: Previous research suggested that patients have increased risk of infection with increased time from presentation with a femoral neck fracture to treatment with a hip hemiarthroplasty (HHA). The purpose of this study was to determine if rates of prosthetic joint infections within 3 months of surgery was affected by the time from patient presentation with a femoral neck fracture to the time of treatment with HHA. MATERIALS AND METHODS: Acute hip fractures treated with HHA between 2005 and 2017 at three centres in Norway were enrolled in the study. Multi-trauma patients were excluded. Univariable analysis was performed to determine any significant effect of pre-operative waiting time on infection rate. Two pre-planned analyses dichotomizing pre-operative waiting time cut-offs were performed. RESULTS: There were 2300 patients with an average age of 82 (range, 48-100) years included of which 3.4% experienced a prosthetic joint infection within 3 months. The primary analysis found no significant difference in infection rate depending on time to surgery (OR = 1.06 (95% CI 0.94-1.20, p = 0.33)). The secondary analyses showed no significant differences in infection rates when comparing pre-operative waiting time of <24 hours vs ≥24 hours (OR = 0.92 (95% CI 0.58-1.46, p = 0.73)) and <48 hours vs ≥48 hours (OR = 1.39 (95% CI 0.81-2.38, p = 0.23)). CONCLUSION: Based off of a large retrospective Norwegian database of hip fractures there did not appear to be a significant difference in infection rate based on pre-operative wait time to surgery.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".