Diagnostic and therapeutic treatment modalities for acute lower gastrointestinal bleeding: a systematic review
Bibliographic record
Abstract
Abstract Background and study aims Investigations for lower gastrointestinal bleeding (LGIB) include flexible sigmoidoscopy, colonoscopy, computed tomographic angiography (CTA), and angiography. All may be used to direct endoscopic, radiological or surgical treatment, although their optimal use is unknown. The aims of this study were to determine the diagnostic and therapeutic yields of endoscopy, CTA, and angiography for managing LGIB, and their influence on rebleeding, transfusion, and hospital stay. Patients and methods A systematic search of MEDLINE, PubMed, EMBASE, and CENTRAL was undertaken to identify randomized controlled trials (RCTs) and nonrandomized studies of intervention (NRSIs) published between 2000 and 12 November 2015 in patients hospitalized with LGIB. Separate meta-analyses were conducted, presented as pooled odds (ORs) or risk ratios (RR) with 95 % confidence intervals (CIs). Results Two RCTs and 13 NRSIs were included, none of which examined flexible sigmoidoscopy, or compared endotherapy with embolization, or investigated the timing of CTA or angiography. Two NRSIs (57 – 223 participants) comparing colonoscopy and CTA were of insufficient quality for synthesis but showed no difference in diagnostic yields between the two interventions. One RCT and 4 NRSIs (779 participants) compared early colonoscopy (< 24 hours) with colonoscopy performed later; meta-analysis of the NRSIs demonstrated higher diagnostic and therapeutic yields with early colonoscopy (OR 1.86, 95 %CI 1.12 to 2.86, P = 0.004 and OR 3.08, 95 %CI 1.93 to 4.90, P < 0.001, respectively) and reduced length of stay (mean difference 2.64 days, 95 %CI 1.54 to 3.73), but no difference in transfusion or rebleeding. Conclusions In LGIB there is a paucity of high-quality evidence, although the limited studies on the timing of colonoscopy suggest increased rates of diagnosis and therapy with early colonoscopy.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".