Bibliographic record
Abstract
Iron-deficiency anemia is a common reason for referral to a gastroenterologist. In adult men and postmenopausal women, gastrointestinal tract pathology is often the cause of iron-deficiency anemia, so patients are frequently referred for endoscopic evaluation. Endoscopy may be costly and at times difficult for the patient. Therefore, physicians need to know what lesions can be identified reliably and, more importantly, the importance of ruling out life-threatening conditions such as occult malignancy. Over the past decade, a number of prospective studies have been completed that examined the yield of endoscopy in the investigation of iron-deficiency anemia. The present article provides a broad overview of iron-deficiency anemia, with particular emphasis on hematological diagnosis, etiology, the use of endoscopy in identifying lesions and iron-repletion therapy. Other clinical scenarios, including assessment of patients on anti-inflammatory or anticoagulation therapy and patients with bleeding of obscure origin, are also addressed. The present article provides a diagnostic algorithm to iron-deficiency anemia, which describes a more systematic manner in which to approach iron-deficiency anemia.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".