Frequency and timing of all‐cause deaths in visits involving suspected transfusion reactions, and the significance of cardiopulmonary disturbances
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Transfusion reactions (TRs) may cause or contribute to death. Cardiopulmonary TRs are distressing, and collectively account for most transfusion fatalities, though the degree to which they alter survival more broadly is unclear. Deaths (and their timing) after TRs may provide further insights. MATERIALS/METHODS: Adult (tri-hospital network) haemovigilance data (2013-2016) recorded referrals with conclusions ranging from unrelated to transfusion (UTR) to entities such as: septic TRs, serologic/haemolytic reactions, transfusion-associated circulatory overload (TACO), transfusion-associated dyspnoea (TAD), transfusion-related acute lung injury (TRALI), allergic transfusion reaction (ATR), and others. For (in- or out-patient) visits involving suspected TRs (VISTRs), all-cause mortalities (% [95% confidence interval]) and associated time-to-death (TTD) (median days, [interquartile range]) were compared. Diagnoses were defined inclusively (possible-to-definite) or strictly (probable-to-definite). RESULTS: Of 1144 events, rank order VISTR mortality following (possible-to-definite) TRs, and associated TTDs, were led by: DHTR 33% [6-19], 1 death at 123d; TRALI 32% [15-54], 6 deaths: 3d [2-20]; BaCon 21% [14-31], 17 deaths: 10d [3-28]; TACO 18% [12-26], 23 deaths: 16d [6-28]; TAD 17% [11-26]: 18 deaths, 6d [3-12]. Higher-certainty TRs ranked similarly (DHTR 50% [9-91]; BaCon 29% [12-55], 4 deaths: 12d [3-22]; and TACO 25% [16-38], 15 deaths: 21d [6-28]). VISTR mortality after TACO or TRALI significantly exceeded ATR (3·3% [2·4-5·8], P < 0·00001) but was not different from UTR events (P = 0·3). CONCLUSIONS: Only half of cardiopulmonary TRs constituted high certainty diagnoses. Nevertheless, cardiopulmonary TRs and suspected BaCon marked higher VISTR mortality with shorter TTDs. Short (<1 week) TTDs in TAD, BaCon or TRALI imply either contributing roles in death, treatment refractoriness and/or applicable TR susceptibilities in the dying.
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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 | 0.006 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".