Mini‐Sentinel's systematic reviews of validated methods for identifying health outcomes using administrative and claims data: methods and lessons learned
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To overview the methods used in the Mini-Sentinel systematic reviews of validation studies of algorithms to identify health outcomes in administrative and claims data and to describe lessons learned in the development of search strategies, including their ability to identify articles from previous systematic reviews which used different search strategies. METHODS: Literature searches were conducted using PubMed and the citation database of the Iowa Drug Information Service. Embase was searched for some outcomes. The searches were based on a strategy developed by the Observational Medical Outcomes Partnership (OMOP) researchers. All citations were reviewed by two investigators. Exclusion criteria were applied at abstract and full-text review stages to ultimately identify algorithm validation studies that used data sources from the USA or Canada, as the results of these studies were considered most likely to generalize to Mini-Sentinel data. Nonvalidated algorithms were reviewed if fewer than five algorithm validation studies were identified. RESULTS: The results of this project are described in the separate articles and reports written on algorithms to identify each outcome of interest. CONCLUSIONS: The Mini-Sentinel systematic reviews of algorithms to identify health outcomes in administrative and claims data are expected to be relatively complete, despite some limitations. Algorithm validation studies are inconsistently indexed in PubMed, creating challenges in conducting systematic reviews of these studies. Google Scholar searches, which can perform text word searches of electronically available articles, are suggested as a strategy to identify studies that are not captured through searches of standard citation databases.
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.
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 | 0.565 | 0.139 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it