Safety of a ‘swing room’ surgery model at a high-volume hip and knee arthroplasty centre
Bibliographic record
Abstract
AIMS: The practice of overlapping surgery has been increasing in the delivery of orthopaedic surgery, aiming to provide efficient, high-quality care. However, there are concerns about the safety of this practice. The purpose of this study was to examine the safety and efficacy of a model of partially overlapping surgery that we termed 'swing room' in the practice of primary total hip (THA) and knee arthroplasty (TKA). METHODS: A retrospective review of prospectively collected data was carried out on patients who underwent primary THA and TKA between 2006 and 2017 in two academic centres. Cases were stratified as partially overlapping (swing room), in which the surgeon is in one operating room (OR) while the next patient is being prepared in another, or nonoverlapping surgery. The demographic details of the patients which were collected included operating time, length of stay (LOS), postoperative complications within six weeks of the procedure, unplanned hospital readmissions, and unplanned reoperations. Fisher's exact, Wilcoxon rank-sum tests, chi-squared tests, and logistic regression analysis were used for statistical analysis. RESULTS: A total of 12,225 cases performed at our institution were included in the study, of which 10,596 (86.6%) were partially overlapping (swing room) and 1,629 (13.3%) were nonoverlapping. There was no significant difference in the mean age, sex, body mass index (BMI), side, and LOS between the two groups. The mean operating time was significantly shorter in the swing room group (58.2 minutes) compared with the nonoverlapping group (62.8 minutes; p < 0.001). There was no significant difference in the rates of complications, readmission and reoperations (p = 0.801 and p = 0.300, respectively) after adjusting for baseline American Society of Anesthesiologists scores. CONCLUSION: 2020;102-B(7 Supple B):112-115.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".