A systematic review of validated methods for identifying infection related to blood products, tissue grafts, or organ transplants using administrative data
Bibliographic record
Abstract
PURPOSE: To systematically review algorithms to identify infections related to blood products, tissue grafts, or organ transplants in administrative and claims data, focusing on studies that have examined the validity of the algorithms. METHODS: A literature search was conducted using PubMed and the database of the Iowa Drug Information Service. 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: Searches identified one study that examined the validity of an algorithm to identify aspergillosis in transplant recipients and 16 studies that used nonvalidated algorithms to identify infections in recipients of blood products, tissue grafts, or organ transplants. Transfusion was studied as a risk factor for infection, but no studies attempted to identify infection transmitted by any of the exposures under review. Two studies reported sensitivity ranging from 21% to 83% and specificity of 100% of codes to identify allogeneic blood transfusion. No validation studies of algorithms to identify tissue grafts or organ transplant were identified. CONCLUSIONS: There is little evidence to support the validity of algorithms to identify infections related to blood products, tissue grafts, or organ transplants in administrative data or algorithms to identify the exposures. Although it may be possible to validate algorithms to identify the exposures and infectious outcomes, the use of administrative data to identify infections transmitted by these exposures may be challenging. Codes indicating infections acquired through medical care may be useful.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".