Associated criteria used in investigating suspected septic transfusion reactions: A scoping review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Septic transfusion reactions (STRs) occur as a result of bacterial contamination of blood or blood products, resulting in sepsis. This scoping review aimed to identify, explore and map the available literature on the STR criteria triggering the investigation of STR. MATERIALS AND METHODS: Four electronic databases (MEDLINE, Web of Science, Science Direct, Embase) were searched to retrieve scientific literature reporting such criteria, published from 1 January 2000 to 5 May 2022. Grey literature was also searched from open web sources. RESULTS: Of 1052 references identified, 43 (21 peer-reviewed and 22 grey literature) met the eligibility criteria for inclusion and data extraction after full article screening. Of them, most (27/43, 62.79%) were found to report a single set of criteria, and only two reported four or more sets of criteria. The analysis of 66 sets of criteria collected from the selected references revealed 57 different sets. A few sets of criteria used only one sign and symptom (s/s) (12.12%, n = 8), whereas 16 sets used 7-15 s/s (n = 16/66; 24.24%). Of the total 319 occurrences of s/s associated with the 66 sets of criteria, post-transfusion hyperthermia, body temperature increase and hypotension were the most common s/s categories. Of all the literature available, only one study tested the diagnostic accuracy of the STR criteria. CONCLUSION: This scoping review revealed a substantial variation in criteria used to identify suspected STR. Consequently, conducting further studies to enhance the diagnostic accuracy of these criteria, which trigger STR investigations, is imperative for advancing clinical practice.
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 | 0.037 | 0.175 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.050 | 0.040 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".