Hemoglobin matters: Perioperative blood management for oncology patients
Bibliographic record
Abstract
As the number of cancer cases rise each year in Canada, so does the number of surgical oncology cases. Surgery presents a unique and heightened stressor for the body already experiencing volatility from factors such as disease and treatments. Perioperative red blood cell (RBC) transfusions are critical to stabilize hemoglobin levels and correct anemia, as well as provide a buffer against anticipated intraoperative blood loss. Thoroughly examining and anticipating risk factors related to the potential need for perioperative blood transfusions is necessary to improve outcomes. Research evidence in recent years related to perioperative blood management of oncology patients has specifically recommended active, coordinated programs to reduce the need and amount of blood transfusions administered pre-, intra-, and post-surgery. Coordination between surgical oncologists and a local or provincial patient blood management (PBM) program is an important strategy that allows patients at risk of perioperative complications to be identified and receive early interventions and ongoing observation.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".