Mini‐Sentinel's systematic reviews of validated methods for identifying health outcomes using administrative data: summary of findings and suggestions for future research
Bibliographic record
Abstract
PURPOSE: The validity of findings from surveillance activities, which use administrative and claims data to link exposures to adverse events, depends in part on the validity of algorithms to identify health outcomes using these data. This review provides a high level overview of the findings of 19 systematic reviews of studies, which have examined the validity of algorithms to identify health outcomes using these data. The author categorized outcomes on the basis of the strength of evidence supporting valid algorithms to identify acute or incident events and suggested priorities for future validation studies. METHODS: The 19 reviews were evaluated, and key findings and suggestions for future research were summarized by a single reviewer. Outcomes with algorithms that consistently identified acute events or incident conditions with positive predictive values of greater than 70% across multiple studies and populations are described as low priority for future algorithm validation studies. RESULTS: Algorithms to identify cerebrovascular accidents, transient ischemic attacks, congestive heart failure, deep vein thrombosis, pulmonary embolism, angioedema, and total hip arthroplasty revision performed well across multiple studies and are considered low priority for future validation studies. Other outcomes were generally thought to require additional validation studies or algorithm refinement to be confident in algorithms. Few studies examined the validity of International Classification of Diseases, 10th Revision, codes. CONCLUSION: Users of these reviews need to consider the generalizability of findings to their study populations. For some outcomes with poorly performing codes, it may always be necessary to validate cases.
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.
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 | 0.147 | 0.384 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.026 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".