Risk of acquiring Creutzfeldt-Jakob disease from blood transfusions: systematic review of case-control studies
Bibliographic record
Abstract
OBJECTIVE: To determine the strength of association between history of blood transfusion and development of Creutzfeldt-Jakob disease. DATA SOURCES: English and non-English language articles published from January 1966 to January 1999 were retrieved using a keyword search of Medline and Embase. These were supplemented by handsearching key journals and searching bibliographies of reviews. STUDY SELECTION: Two independent reviewers selected the relevant abstracts and articles. Articles were chosen that reported the results of case-control studies trying to identify rates of prior blood transfusion in patients with Creutzfeldt-Jakob disease and in controls. DATA EXTRACTION: Odds ratios and information on study quality were extracted from the selected articles by two independent reviewers. DATA SYNTHESIS: Five studies containing data on 2479 patients were included. Three of the five studies used medical or neurological patients as controls, the other two used population controls. Odds ratios for developing Creutzfeldt-Jakob disease from blood transfusion ranged from 0.54 to 0.89. Four of the five studies had confidence intervals that crossed 1.0. The combined odds ratio was 0.70 (95% confidence interval 0.54 to 0.89). CONCLUSIONS: Case-control studies do not suggest a risk of developing Creutzfeldt-Jakob disease from blood transfusion. Rather, a trend seems to exist towards a lower frequency of previous blood transfusion in patients with Creutzfeldt-Jakob disease than in controls. However, it is important to be aware of these studies' methodological limitations-primarily the choice of control population and reliability of recall of transfusion status.
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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 | 0.020 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.008 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".