Reducing unnecessary red blood cell transfusion in hospitalised patients
Bibliographic record
Abstract
### What you need to know Transfusion of red blood cells (RBC) is used to treat patients with severe anaemia or bleeding. Supplies of blood for transfusion need to be protected, as countries have experienced a decline in donation rates during the covid-19 pandemic.1 However, published international audits describe inappropriate rates of RBC transfusion of 22-57% in a variety of clinical settings, including hospitalised inpatients, operative units, and emergency departments.234 Unnecessary blood transfusions may expose patients to harms, including allergic, febrile, or haemolytic reactions; circulatory overload associated with transfusion (seen in up to 1-6% of transfused patients); and acute lung injury.5 These complications may occur without the transfusion adding any clinical benefit. Blood products are costly to collect and administer. Their overuse wastes a limited precious resource donated by the public.6 Conservative blood use, often referred to as “restrictive transfusion practice,” is recommended in stable, non-bleeding patients by the National Institute for Health and Care Excellence (NICE) and the Choosing Wisely campaigns in Canada, the UK, and the US.78910 Recommendations focus on two major clinical decision points: the haemoglobin concentration (Hb) at which blood transfusion is considered, and the number of RBC units administered at a time (table 1). View this table: Table 1 When to transfuse RBCs in the adult inpatient78 …
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".