Low levels of interleukin-10 in patients with transfusion-related acute lung injury
Bibliographic record
Abstract
Transfusion related acute lung injury (TRALI) is the leading cause of transfusion-related fatalities (FDA Report 2016) (1) and is characterized by the acute onset of respiratory distress within 6 hours following blood transfusion (2,3). The clinical diagnosis is confirmed in case of newly developing acute respiratory distress: PaO 2 /FiO 2 ratio (ratio of arterial oxygen partial pressure to fractional inspired oxygen) <300 mmHg or arterial oxygen saturation <90% at room air; newly developed or worsened bilateral pulmonary infiltrates indicative of pulmonary edema on chest X-ray; emergence of all symptoms within 6 hours upon blood transfusion and exclusion of cardiac ischemia and transfusion associated circulatory overload (TACO). Apart from supportive measures, such as oxygen or ventilation, no specific therapies are available. The pathogenesis is incompletely understood, however, a two-hit model is usually assumed to underlie the disease pathology. The first hit consists of a pre-disposing factor present in the recipient such as inflammation while the second hit is conveyed by factors present in the transfused blood product such as anti-leukocyte antibodies (3). For example, the acute phase protein C-reactive protein (CRP) which rapidly increases during infection and inflammation was shown to enhance antibody-mediated TRALI in mice (4) and in line with that data, CRP was found to be elevated in human TRALI patients (5) confirming its role as a first hit risk factor in human TRALI.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".