The association of fever with transfusion‐associated circulatory overload
Bibliographic record
Abstract
BACKGROUND: Fever is described in transfusion-associated circulatory overload (TACO), reflecting either comprehensive haemovigilance or an inflammatory pathobiology (such as congestion-associated atheroma disruptions). METHODS: Hospital haemovigilance data (1/1/2010-31/12/2012) were reviewed for TACO cases (frequency and mode of referral). TACO with or without fever (TACO+F/-F) was examined for its association with patient age (as a surrogate for atheroma burden) and product age (as a surrogate for storage-related pyrogens). Fever in allergic transfusion reactions was also compared. RESULTS: Of 972 reactions, 107 suspected cases of TACO (11%) were seen. TACO+F vs. TACO-F occurred in 42·1 vs. 57·9%, respectively. TACO+F cases were discovered in referrals to investigate either a fever (in 47·1%) or dyspnoea (in 52·9%). Among TACO+F cases, 24·4% had already been febrile, whereas 75·6% exhibited a new reaction-associated fever. After excluding preexisting fevers, TACO+F occurred in 31·8% of TACO, compared with 8·2% of allergic reactions with fever, for an odds ratio of 5·2 (2·9-9·4 [95% CI]), P < 0·001. TACO+F/TACO-F showed no difference in median host age (69 vs. 64 years, P = 0·3), RBC age (22 days +F/-F, P = 0·9) or severity. CONCLUSION: Transfusion-associated circulatory overload disproportionately exhibits fever compared with allergic reactions. However, TACO+F did not associate with patient or product age, nor reflect severity. To better understand TACO+F, the fever-congestion sequence merits attention. Further study is needed to see whether TACO+F occurs as reproducibly elsewhere, and in association with atherosclerosis in a better characterized cohort.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".