Efficient blood testing in endourology
Bibliographic record
Abstract
Introduction: Type and screen testing (T&S) is routinely performed preoperatively for many endoscopic procedures, despite low transfusion rates. While important, T&S can be costly, unnecessary, and burdensome for patients to obtain in a short timeframe due to expiry. We aimed to assess and reduce unnecessary T&S in a safe and collaborative manner through a Transfusion Dashboard. We assessed the effect of reduced testing on patient safety, cost, and the environment. Methods: This quality improvement study used the Transfusion Dashboard, a web-based, institutional platform tracking blood transfusion trends. During the observation phase (2016-2019), procedure-specific preoperative T&S recommendations were developed. Following implementation of these recommendations in 2020, the incidence of T&S, perioperative transfusion rates, and rescue transfusion rates were assessed pre- and post-intervention using the Chi-squared test. Cost and environmental savings were also evaluated. Results: From 2016-2023, outcomes were tracked for 4375 pre-initiative and 2488 post-initiative patients who underwent endoscopic procedures. We found a statistically significant decrease in T&S following initiative implementation for transurethral resection of the prostate (TURP), percutaneous nephrolithotomy (PCNL), holmium e-nucleation of the prostate (HoLEP), and transurethral resection of bladder tumor (TURBT) by as much as 51.2%. There was no change in uncrossed or overall blood transfusions. Since the implementation of the initiative, $45 362.81 in testing materials were saved and an associated reduction of 697 kg CO2 was observed. Conclusions: Institutional- and procedure-specific testing guidelines decreased unnecessary tests, leading to improved resource stewardship, reduced cost, improved patient experience, and environmental savings. Initial modest cost savings and 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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".