Bibliographic record
Abstract
Occult gastrointestinal bleeding is defined as gastrointestinal bleeding that is not visible to the patient or physician, resulting in either a positive fecal occult blood test, or iron deficiency anemia with or without a positive fecal occult blood test. A stepwise evaluation will identify the cause of bleeding in the majority of patients. Esophagogastroduodenoscopy (EGD) and colonoscopy will find the bleeding source in 48 to 71 percent of patients. In patients with recurrent bleeding, repeat EGD and colonoscopy may find missed lesions in 35 percent of those who had negative initial findings. If a cause is not found after EGD and colonoscopy have been performed, capsule endoscopy has a diagnostic yield of 61 to 74 percent. Deep enteroscopy reaches into the mid and distal small bowel to further investigate and treat lesions found during capsule endoscopy or computed tomographic enterography. Evaluation of a patient who has a positive fecal occult blood test without iron deficiency anemia should begin with colonoscopy; asymptomatic patients whose colonoscopic findings are negative do not require further study unless anemia develops. All men and postmenopausal women with iron deficiency anemia, and premenopausal women who have iron deficiency anemia that cannot be explained by heavy menses, should be evaluated for occult gastrointestinal bleeding. Physicians should not attribute a positive fecal occult blood test to low-dose aspirin or anticoagulant medications without further evaluation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.986 | 0.988 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".