Factors influencing the yield of mesenteric angiography in lower gastrointestinal bleed
Bibliographic record
Abstract
AIM: To assess if certain triaging rules could be established to optimize the yield of mesenteric angiography. METHODS: Medical records of 101 patients were retrospectively reviewed and parameters relating to age, gender, pulse rate, blood pressure, serum hemoglobin, intensive care unit (ICU) admission, and the number of packed red blood cells (PRBC) transfused in the 12 and 24 h prior to the angiography were tabulated in two groups with positive and negative angiography results. RESULTS: We found no correlation between gender, pulse rate, blood pressure or serum hemoglobin and positivity of the mesenteric angiogram. But patients with positive angiogram were found to be on average 7 years older (73.2 years vs 65.9 years old) (P = 0.02). Angiogram was positive in 39.3 % (11/28) of patients admitted in ICU vs 23.2% (17/73) who were admitted elsewhere in the hospital (P = 0.03). In the 12 and 24 h prior to angiography, patients with a positive angiogram received a mean of 2.7 ± 2.3 and 3.3 ± 2.6 units of PRBC s respectively, while patients with a negative angiogram had a mean of 1.6 ± 1.9 (P = 0.02) and 2.1 ± 2.6 units (P = 0.04) received respectively in the same period. CONCLUSION: Older age, ICU admission, having received at least 4 units PRBC over 12 h or 5 units over 24 h prior to angiogram are leading indicators for a positive study.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".