Evaluation of Transfusion Pyrexia: A Review of Differential Diagnosis and Management
Bibliographic record
Abstract
Background/purpose. Transfusion pyrexia (fever) is an important clinical sign/symptom occurring either as an isolated event or as part of a constellation of signs and symptoms in relation to blood transfusion. It is an important cause of morbidity and may be an important sign of life-threatening complications of blood transfusion. Pyrexia is often a reason for the discontinuation of a blood transfusion episode, and adequate evaluation remains a challenge for clinicians. The decision to stop a blood transfusion episode on account of fever is often a difficult one. This paper reviews the differential diagnosis of transfusion pyrexia (TP), the pathogenesis as well as current management measures. Study selection and data source. Literature sources include medical texts, journals, dissertations, and internet-based electronic materials Results and conclusion. Adequate evaluation of pyrexia accompanying blood transfusion remains a challenge for clinicians. An algorithm to assist the clinician in the evaluation of fever occurring in a blood transfusion recipient is developed and presented. Continuous medical education is necessary for clinicians towards improved patient care in transfusion medicine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".