Association between surgical wait time and hospital length of stay in primary total knee and hip arthroplasty
Bibliographic record
Abstract
AIMS: In countries with social healthcare systems, such as Canada, patients may experience long wait times and a decline in their health status prior to their operation. The aim of this study is to explore the association between long preoperative wait times (WT) and acute hospital length of stay (LoS) for primary arthroplasty of the knee and hip. METHODS: The study population was obtained from the provincial Patient Access Registry Nova Scotia (PARNS) and the Canadian national hospital Discharge Access Database (DAD). We included primary total knee and hip arthroplasties (TKA, THA) between 2011 and 2017. Patients waiting longer than the recommended 180 days Canadian national standard were compared to patients waiting equal or less than the standard WT. The primary outcome measure was acute LoS postoperatively. Secondarily, patient demographics, comorbidities, and perioperative parameters were correlated with LoS with multivariate regression. RESULTS: A total of 11,833 TKAs and 6,627 THAs were included in the study. Mean WT for TKA was 348 days (1 to 3,605) with mean LoS of 3.6 days (1 to 98). Mean WT for THA was 267 days (1 to 2,015) with mean LoS of 4.0 days (1 to 143). There was a significant increase in mean LoS for TKA waiting longer than 180 days (2.5% (SE 1.1); p = 0.028). There was no significant association for THA. Age, sex, surgical year, admittance from home, rural residence, household income, hospital facility, the need for blood transfusion, and comorbidities were all found to influence LoS. CONCLUSION: 2021;2(8):679-684.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".