Bibliographic record
Abstract
Introduction: group and screen (g&s) testing is routinely performed preoperatively for many endoscopic procedures, despite a low rate of blood transfusion.While important, testing can be costly, unnecessary, and burdensome to patients to obtain g&s in a shor t timeframe due to expiry.We aimed to assess and reduce unnecessary g&s testing in a safe and collaborative manner through a transfusion dashboard.We assessed the effect of reduced g&s testing on cost and the environment.Methods: the transfusion dashboard (td), launched at UBC in 2020, is a quality improvement initiative that tracks procedure-specific transfusion rates.Based on initial findings, recommendations for preoperative g&s for endoscopic procedures were developed.We reviewed the incidence of g&s testing, perioperative transfusion rates, and rescue transfusion rates (when a patient without g&s needs urgent, uncrossed transfusion) in endourologic procedures before and after the implementation of this initiative with the Chi-squared test.We also assessed cost and environmental savings.Results: From 2016-2023, outcomes were tracked for 4393 pre-td initiative and 2058 post-td initiative patients who underwent endoscopic procedures (table 1).We found a statistically significant decrease in g&s testing post-td for tUrp, pnl, Holep, and tUrBt by as much as 66.5% (p<0.001)(table 2).there was no change in uncrossed or overall blood transfusions (0% for all groups).In 2022 alone, the overall cost saving across these four procedures was $11 580.89, while the overall environment saving was 178 Kg Co2, which is equivalent to 199 pounds of coal burned.Conclusions: Institutional and procedure-specific g&s testing guidelines decrease unnecessary tests, leading to improved resource stewardship, reduced cost, respecting patients' time, and environmental savings.While the cost saving per group is modest, care improvements may be amplified safely in larger organizations and across more procedures.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.388 | 0.045 |
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".