The incidence of perioperative anemia and iron deficiency in patients undergoing gyne-oncology surgery
Bibliographic record
Abstract
Preoperative anemia is progressively being recognized as a risk factor for poor perioperative outcomes including increased length of hospital stay and increased blood transfusions. The growth in prevalence of preoperative anemia in patients undergoing gynecological oncology procedures warrants greater attention to early identification for optimal surgical outcomes. This was a quantitative retrospective observational study consisting of 284 patients undergoing gynecological oncology procedures. The study sought to determine the frequency of anemia, iron deficiency and the effect of anemia on the number of blood transfusions from January 1 to December 31, 2014. Patients with anemia had significantly higher transfusion rates (44% versus 11%, p < 0.0001), considerably higher number of units transfused per patient (mean 1.19 units versus 0.28 units, p < 0.0001) and longer length of stays post-operatively (mean 5.9 days versus 4.6 days, p=0.0008). It was concluded that early identification and treatment of anemia is a key opportunity to optimized surgical 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".