Mesenteric angiography for the localization and treatment of acute lower gastrointestinal bleeding.
Bibliographic record
Abstract
BACKGROUND: There is limited evidence regarding the effectiveness and complications of mesenteric angiography in the diagnosis and management of acute lower gastrointestinal bleeding (ALGIB). Our objective was to determine the complications and outcomes of mesenteric angiography in patients with ALGIB and to identify predictors of a positive result at angiography. METHODS: We identified and reviewed the records of all patients who underwent mesenteric angiography for ALGIB at our institution during a 10-year period. We compared potential predictors of positive versus negative angiograms. RESULTS: Of 47 mesenteric angiograms in 35 patients, 22 (47%, 95% confidence interval [CI] 33%-61%) revealed a source of bleeding, most commonly the colon. Hematomas developed in the groins of 3 patients (6.4%, 95% CI 0%-18%), and 1 of these patients also experienced a myocardial infarction during the procedure. None of the potential predictors were significantly associated with a positive result at angiography, although the confidence intervals were wide. Twenty patients (57%, 95% CI 41%-74%) continued to bleed after the angiogram, and 18 of the patients (51%, 95% CI 35%-68%) were discharged without a definitive diagnosis. CONCLUSION: With a diagnostic success of about 50%, mesenteric angiography may play an important part in the diagnosis and management of patients with ALGIB; however, one or more large, prospective multicentre studies are needed to more clearly define its role. Canadian surgeons have the opportunity to initiate collaborative multicentre studies to address such diagnostic and therapeutic clinical questions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".