Reducing AB plasma utilisation through the AB plasma appropriateness index
Bibliographic record
Abstract
OBJECTIVES: We hypothesised that there was inappropriate group AB plasma used in our hospital, identifiable by a novel key quality indicator (KQI) and mitigable through massive transfusion protocol (MTP) modification. BACKGROUND: Group AB plasma is a scarce resource strained by increasing usage worldwide when used as universal donor plasma in non-group AB patients. To reduce inappropriate use and to promote benchmarking to the best practice, we developed the AB plasma appropriateness index (ABAI). ABAI is the ratio of AB plasma transfused to group AB or unknown blood group patients to all AB plasma utilised, where values closer to 1 are better. METHODS: Data collected included AB plasma disposition by blood group, indications for transfusion, total blood utilisation, patient clinical characteristics and outcomes. ABAI during a 12-month period was retrospectively assessed, which led to implementation of pre-thawed group A plasma instead of group AB plasma for trauma patients starting in July 2017. RESULTS: The ABAI retrospectively showed inappropriate use in non-group AB patients in our hospital, the majority used to avoid expiry after thaw. When comparing 1-year pre- and post-implementation periods, ABAI improved from 0·464 to 0·900 (P < 0·0001). After exclusion of therapeutic plasma exchange, ABAI still improved (0·486-0·720, P < 0·0001). No differences in the length of stay or mortality associated in 32 patients receiving group A plasma for emergency release were observed. CONCLUSION: The ABAI is a novel KQI to indicate inappropriate AB plasma usage for quality improvement. This led to thawed A plasma use for MTPs, reducing inappropriate AB plasma usage.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".