Identifying Pediatric Diabetes Cases from Health Administrative Data: A Population-Based Validation Study in Quebec, Canada [Corrigendum]
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
Nakhla M, Simard M, Dube M, et al. Clin Epidemiol. 2019;11:833–843.\nThe authors have advised there is an error in the diagnostic codes used to validate the cases of diabetes. The code “251.X” was never included in the validation algorithm and was erroneously included in the list of ICD-9 codes in the final revision phase of the manuscript. The authors apologize for this error.\nPage 835, Diagnostic accuracy section, second sentence, the text “We determined the diagnostic accuracy (sensitivity, specificity, PPV, NPV) of a variety of algorithms, using combinations of physician billings and hospital admissions over 1 or 2 years bearing a diagnosis code of diabetes mellitus (ICD-9 250.X, 251.X; ICD-10 E10.X-14. X)” should read “We determined the diagnostic accuracy (sensitivity, specificity, PPV, NPV) of a variety of algorithms, using combinations of physician billings and hospital admissions over 1 or 2 years bearing a diagnosis code of diabetes mellitus (ICD-9 250.X; ICD-10 E10.X-14. X)”.\nRead the original article
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it