Cost‐effectiveness analysis of preoperative transfusion in patients with sickle cell disease using evidence from the <scp>TAPS</scp> trial
Bibliographic record
Abstract
The study's objective was to assess the cost-effectiveness of preoperative transfusion compared with no preoperative transfusion in patients with sickle cell disease undergoing low- or medium-risk surgery. Seventy patients with sickle cell disease (HbSS/Sß(0) thal genotypes) undergoing elective surgery participated in a multicentre randomised trial, Transfusion Alternatives Preoperatively in Sickle Cell Disease (TAPS). Here, a cost-effectiveness analysis based on evidence from that trial is presented. A decision-analytic model is used to incorporate long-term consequences of transfusions and acute chest syndrome. Costs and health benefits, expressed as quality-adjusted life years (QALYs), are reported from the 'within-trial' analysis and for the decision-analytic model. The probability of cost-effectiveness for each form of management is calculated taking into account the small sample size and other sources of uncertainty. In the range of scenarios considered in the analysis, preoperative transfusion was more effective, with the mean improvement in QALYs ranging from 0.018 to 0.206 per patient, and also less costly in all but one scenario, with the mean cost difference ranging from -£813 to £26. All scenarios suggested preoperative transfusion had a probability of cost-effectiveness >0.79 at a cost-effectiveness threshold of £20 000 per QALY.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".