A systematic review of validated methods for identifying transfusion‐related ABO incompatibility reactions using administrative and claims data
Bibliographic record
Abstract
PURPOSE: This paper aimed to systematically review algorithms to identify transfusion-related ABO incompatibility reactions in administrative data, with a focus on studies that have examined the validity of the algorithms. METHODS: A literature search was conducted using PubMed, Iowa Drug Information Service database, and Embase. A Google Scholar search was also conducted because of the difficulty identifying relevant studies. Reviews were conducted by two investigators to identify studies using data sources from the USA or Canada because these data sources were most likely to reflect the coding practices of Mini-Sentinel data sources. RESULTS: One study was found that validated International Classification of Diseases (ICD-9-CM) codes representing transfusion reactions. None of these cases were ABO incompatibility reactions. Several studies consistently used ICD-9-CM code 999.6, which represents ABO incompatibility reactions, and a technical report identified the ICD-10 code for these reactions. One study included the E-code E8760 for mismatched blood in transfusion in the algorithm. Another study reported finding no ABO incompatibility reaction codes in the Healthcare Cost and Utilization Project Nationwide Inpatient Sample database, which contains data of 2.23 million patients who received transfusions, raising questions about the sensitivity of administrative data for identifying such reactions. Two studies reported perfect specificity, with sensitivity ranging from 21% to 83%, for the code identifying allogeneic red blood cell transfusions in hospitalized patients. CONCLUSIONS: There is no information to assess the validity of algorithms to identify transfusion-related ABO incompatibility reactions. Further information on the validity of algorithms to identify transfusions would also be useful.
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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 | 0.050 | 0.243 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.034 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 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".