The Use of Tranexamic Acid for Upper Gastrointestinal Bleeding by Medical and Surgical Intensivists: A Single Center Experience
Bibliographic record
Abstract
BACKGROUND: Tranexamic acid (TXA) may be beneficial in the management of upper gastrointestinal bleeding (UGIB). We sought to investigate how frequently intensivists at our academic institution use TXA for patients with UGIB, and to investigate whether the utilization rate of TXA differs between surgical and medical intensivists, and provide an updated literature review on the subject. METHODS: We performed a retrospective cohort study of patients admitted for UGIB to the surgical intensive care unit (SICU) and the medical intensive care unit (MICU) at our academic healthcare facility (University of Florida Health - Shands Hospital) from January 1, 2013 to December 31, 2016. The patients were categorized as receiving or not receiving TXA. The overall utilization rate of TXA was calculated, and the utilization rates for the MICU and SICU were compared using a two-sample test for equality of two proportions with continuity correction. RESULTS: The study cohort included a total of 1,829 patients with a diagnosis of UGIB. Of those, 988 were treated in the MICU and 841 were treated in the SICU. Of the 988 patients in the MICU, six received TXA (0.61%), while 10 (1.19%) of the 841 patients in the SICU received TXA. The overall utilization rate of TXA was 0.87%. The odds of receiving TXA in the SICU were 1.97 times greater than in the MICU (odds ratio (OR): 1.97, 95% confidence interval (CI): 0.74 - 5.2, P = 1.83). CONCLUSIONS: Our study suggests that TXA may be underused in the management of UGIB, and that the utilization rate does not differ significantly between surgical and medical intensivists.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".