Analyzing the temporal trends of kidney transplantation surgeries and their impact on warm and cold ischemia time in a Canadian setting
Bibliographic record
Abstract
<h3>Background:</h3> The effect of weekend admission and surgery on patient morbidity and mortality has been studied in many settings and has been shown to lead to worse outcomes. Several studies have sought to determine whether there is a weekend effect in kidney transplantation specifically, but a clear effect on outcomes and graft survival has not been established. <h3>Methods:</h3> We analyzed data from all deceased-donor organ procurements and cadaveric kidney transplants occurring during the 5-year period between Apr. 1, 2013, and Dec. 31, 2017, included in the database of the Trillium Gift of Life Network, Ontario’s organ and tissue donation agency. <h3>Results:</h3> A total of 1116 deceased donor nephrectomies (DNs) and 1858 recipient procedures were performed in Ontario during the study period. The overall rate of after-hours DNs on weekdays was significantly greater than during working hours (23.1/30 d v. 15.4/30 d, <i>p</i> < 0.001). Donations after neurological determination of death were more frequent during weekday working hours (22.8/30 d) than after hours on weekdays (17.3/30 d, <i>p</i> < 0.001) or weekends (16.3/30 d, <i>p</i> < 0.001), whereas donations after cardiac death were significantly more frequent after hours on weekdays than during working hours (10.3/30 d v. 7.7/30 d, <i>p</i> = 0.021). On weekdays, mean warm ischemia time (WIT) was significantly longer after hours than during working hours (40.75 ± 12.26 min v. 38.52 ± 11.92 min, <i>p</i> = 0.017). Similarly, mean WIT was longer after hours than during working hours on weekends (40.23 ± 12.48 min v. 38.59 ± 11.91 min, <i>p</i> = 0.015). <h3>Conclusion:</h3> Kidney transplantations occurred more frequently after hours and were associated with increased WIT. Further study is needed across multiple Canadian centres to better understand the temporal patterns of kidney transplantation and implications for patients, providers, and health care systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".