Surgical duration is associated with an increased risk of periprosthetic infection following total knee arthroplasty: A population-based retrospective cohort study
Bibliographic record
Abstract
Background Total knee arthroplasty (TKA) is one the most common elective procedures in the world. Post-operative infection is one of its most devastating complications, often necessitating multiple additional surgeries. We aimed to describe the relationship between surgical duration and risk of deep infection following primary elective TKA. Methods In this cohort study we analyses primary TKAs done between 2009 and 2016 in Ontario, Canada. We utilized restricted cubic splines to identify a threshold of surgical duration that was associated with an increased risk for infection requiring surgery. Patients with a ‘short' duration of surgery were matched to those with a ‘long' duration on patient age (±3 years), patient sex, severe obesity (BMI > 40), the primary surgeon, the hospital and the type of anesthetic. Findings In 92,343 primary TKAs, the median surgical duration was 106 min. We identified a cut-point of 100 min that was associated with an increased risk for infection. Subsequently, 17,815 TKA recipients with a ‘long' procedure length were matched to those with a ‘short' procedure length. ‘Long' procedures had a higher rate of deep infection (1.1% versus 0.6%, p < 0.0001). This was equal to a relative risk of 1.81 ( p < 0.0001). Interpretation In a cohort of TKA recipients, we found that procedure lengths longer than 100 min were associated with a significantly increased risk of deep infection requiring surgery. This time threshold serves a useful time-point to identify patients that require closer surveillance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".