Developing a reference standard for pertussis by applying a stratified sampling strategy to electronic medical record data
Bibliographic record
Abstract
PURPOSE: Pertussis surveillance remains essential in Canada, but ascertainment bias limits the accuracy of surveillance data. Introducing other sources to improve detection has highlighted the importance of validation. However, challenges arise due to low prevalence, and oversampling suspected cases can introduce partial verification bias. The aim of this study was to build a reference standard for pertussis validation studies that provides adequate analytic precision and minimizes bias. METHODS: We used a stratified strategy to sample the reference standard from a primary care electronic medical record cohort. We incorporated abstractor notes into definite, possible, ruled-out, and no mention of pertussis classifications which were based on surveillance case definitions. RESULTS: We abstracted eight hundred records from the cohort of 404,922. There were 208 (26%) definite and 261 (32.6%) possible prevalent pertussis cases. Classifications demonstrated a wide variety of case severities. Abstraction reliability was moderate to substantial based on Cohen's kappa and raw percent agreement. CONCLUSIONS: When conducting validation studies for pertussis and other low prevalence diseases, this stratified sampling strategy can be used to develop a reference standard using limited resources. This approach mitigates verification and spectrum bias while providing sufficient precision and incorporating a range of case severities.
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 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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".