Transitioning from night to day transplants: changing the transplant culture for optimisation through a province-wide quality improvement initiative
Bibliographic record
Abstract
BACKGROUND: Organ transplants, some of the most complex operations, have historically been performed overnight due to logistical challenges in healthcare systems. Overnight transplants contribute to transplant team burnout and can compromise patient outcomes. A province-wide quality improvement (QI) initiative was launched to increase the daytime operation rate to ≥80%. METHODS: The project was launched in July 2020 by the Daytime Transplant Working Group, including stakeholders from the Organ Donation Organization, donor hospitals, and the two recipient hospitals to increase daytime transplants, as defined by operation start time between 07:00 and 14:00 for donor operations and 08:00 and 18:00 for recipient operations. Organ donor and recipient operation start times were collected from January 2019 to June 2020 (control period), July 2020 to December 2021 (intervention period) and January 2022 to December 2023 (maintenance period). Data were analysed using p-charts on SQCpack V.7 (PQ Systems, Dayton, OH). RESULTS: From 696 retrieval operations, a total of 458 liver transplants, 295 lung transplants, 126 heart transplants and 1122 kidney transplants were performed. Our ≥80% target was met for liver, lung, and heart transplants; however, there was no change in kidney transplants. Daytime rates of organ donor operations increased but did not achieve the ≥80% target. INTERPRETATION: Despite the COVID-19 pandemic impacting OR access during the intervention period, the target of ≥80% daytime operations was achieved for liver, lung and heart transplants by engaging donor and recipient hospitals. The transition to daytime surgery was improved with a dedicated team to systematically address barriers and concerns from donor hospitals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| 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".