Bibliographic record
Abstract
Lower gastrointestinal hemorrhage presents a common indication for hospitalization and account for over 300,000 admissions per year in the United States. Multimodality imaging is often required to aid in localization of the hemorrhage prior to therapeutic intervention if endoscopic treatment fails. Imaging includes computer tomography angiography, red blood cell tagged scintigraphy and conventional angiography, with scintigraphy being the most sensitive followed by computer tomography angiography. Aberrant celio-mesenteric supply occurs in 2% of the population; however failure to identify this may result in failed endovascular therapy. Computer tomography angiography is sensitive for arterial hemorrhage and delineates the anatomy, allowing the treating physician to plan an endovascular approach. If at the time of conventional angiography, the active bleed is not visualized, but the site of bleeding has been identified on computer tomography angiography, provocative angiography can be utilized in order to stimulate bleeding and subsequent targeted treatment. We describe a case of lower gastrointestinal hemorrhage at the splenic flexure supplied by a celio-mesenteric branch in a patient and provocative angiography with tissue plasminogen activator utilized at the time of treatment to illicit the site of hemorrhage and subsequent treatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".