A systematic review of validated methods for identifying erythema multiforme major/minor/not otherwise specified, Stevens–Johnson Syndrome, or toxic epidermal necrolysis using administrative and claims data
Bibliographic record
Abstract
PURPOSE: The Food and Drug Administration's (FDA) Mini-Sentinel pilot program aims to conduct active surveillance to refine safety signals that emerge for marketed medical products. A key facet of this surveillance is to develop and understand the validity of algorithms for identifying health outcomes of interest (HOIs) from administrative and claims data. This paper summarizes the process and findings of the algorithm review of erythema multiforme and related conditions. METHODS: PubMed and Iowa Drug Information Service searches were conducted to identify citations applicable to the erythema multiforme HOI. Level 1 abstract reviews and Level 2 full-text reviews were conducted to find articles that used administrative and claims data to identify erythema multiforme, Stevens-Johnson syndrome, or toxic epidermal necrolysis and that included validation estimates of the coding algorithms. RESULTS: Our search revealed limited literature focusing on erythema multiforme and related conditions that provided administrative and claims data-based algorithms and validation estimates. Only four studies provided validated algorithms and all studies used the same International Classification of Diseases code, 695.1. Approximately half of cases subjected to expert review were consistent with erythema multiforme and related conditions. CONCLUSIONS: Updated research needs to be conducted on designing validation studies that test algorithms for erythema multiforme and related conditions and that take into account recent changes in the diagnostic coding of these diseases.
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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 | 0.251 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| 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".