The effects of a treatment protocol for cardiac surgical patients with excessive blood loss on clinical outcomes
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Excessive blood loss (EBL) is a common complication of cardiac surgery that is associated with adverse events. The objective of this before/after study was to determine whether the implementation of a protocol for management of cardiac surgical patients with EBL was associated with improved clinical outcomes. MATERIALS AND METHODS: In November 2002, a protocol for prompt identification and aggressive management of cardiac surgical patients with EBL was implemented at our institution. The independent relationship between protocol implementation and adverse outcomes was measured by comparing the outcomes of patients who received > or = 4 RBC (red blood cell) units within 1 day of surgery and were operated on before protocol implementation (2000-02) with those operated on after protocol implementation (2003-05), using multivariable logistic regression analysis to control for the effects of confounders. The primary outcome was a composite of adverse events that included death, renal failure, stroke, and sepsis. Bootstrapping was used to confirm the validity of the results. RESULTS: Of the 11,314 patients who underwent surgery during the study period, 1875 (16.6%) received > or = 4 RBC units within 1 day of surgery, with 958 and 917 in the pre- and postprotocol periods, respectively. The composite adverse outcome occurred in 164 (17.1%) patients in the preprotocol period and 115 (12.5%) patients in the postprotocol period (P = 0.005). Protocol implementation was independently associated with reduced odds of the composite adverse outcome (odds ratio 0.67; 95% confidence interval 0.50, 0.91; P = 0.01). This estimate was stable in bootstrap sampling. CONCLUSION: Implementation of a protocol to manage EBL in cardiac surgery was independently associated with improved outcomes.
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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 | 0.029 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".