Not too much, not too little: the TOP trial and the Goldilocks zone of transfusion
Bibliographic record
Abstract
The TOP trial (ClinicalTrials.gov Identifier: NCT03229941) shows that a liberal transfusion threshold after major vascular or general surgery does not reduce mortality or major ischaemic events compared with a restrictive approach, though it may lessen cardiac complications, supporting restrictive yet individualized transfusion strategies in high-risk patients.1,2 Earlier randomized trials -including TRACS, TRICC III, TITRe2 TRISS, FOCUS, and MINT- established that restrictive transfusion strategies (haemoglobin 7–8 g/dL) are safe for most hospitalized and cardiac surgical patients.3–8 However, those studies largely excluded individuals with significant unrevascularized coronary disease or postoperative cardiac vulnerability. Observational data hinted that severe anaemia might provoke ischaemia or decompensated heart failure, but prospective evidence in high-risk surgical populations was lacking.9 The Transfusion Trigger after Operations in High Cardiac Risk Patients (TOP) trial was designed to directly address this uncertainty. A multicentre, pragmatic, randomized controlled trial comparing two transfusion thresholds in postoperative patients with elevated cardiac risk, assessing whether maintaining higher postoperative haemoglobin levels improves survival or reduces ischaemic complications.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".