Bibliographic record
Abstract
Preoperative anemia is associated with increased postoperative morbidity and mortality and with increased risk of perioperative transfusion. It is an important and modifiable risk factor for surgical patients. For high-blood-loss surgery, preoperative anemia is defined as hemoglobin <13 g/dL for both male and female patients. Preoperative anemia is common, ranging from 25% to 40% in large observational studies. The most common treatable cause of preoperative anemia is iron-deficiency anemia; the initial laboratory tests should focus on making this diagnosis. Management of iron-deficiency anemia includes iron supplementation with IV iron therapy when oral iron is ineffective or not tolerated, there is severe anemia, and there is insufficient time to surgery (<4 weeks). In other situations, erythropoiesis-stimulating agents may be considered, particularly for those patients with multiple alloantibodies or religious objections to transfusion. To facilitate the diagnosis and management of preoperative anemia, establishment of preoperative anemia-screening clinics is essential. The goals of management of preoperative anemia are to treat anemia, reduce the need for transfusion, and improve patient outcomes.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".