Statin potency associated with incident diabetes in a real-world evaluation
Bibliographic record
Abstract
Commentary on: Carter AA, Gomes T, Camacho X, et al. Risk of incident diabetes among patients treated with statins: population based study. BMJ 2013;346:f2610[OpenUrl][1][Abstract/FREE Full Text][2] For widely prescribed medications, such as statins, even the development of uncommon adverse events may have significant public health ramifications. Recent meta-analyses of randomised controlled trials (RCTs) have demonstrated an increased incidence of diabetes in patients taking statins. These have demonstrated a dose–effect relationship, but whether different statins have differential effects on the risk of diabetes is still unclear. In this study, Carter and colleagues further investigate the dose–effect relationship of statin-induced diabetes and whether different statins carry distinct risks of diabetes in a large, real-world population. The authors examined healthcare records of more than 1.5 million adults aged 66 years or above in Ontario, Canada from August 1997 to March 2010. The analysis excluded patients with established diabetes and those prescribed a statin within 1 year prior to the start of the study. Linked administrative healthcare databases were used to provide information on … [1]: {openurl}?query=rft.jtitle%253DBMJ%26rft_id%253Dinfo%253Adoi%252F10.1136%252Fbmj.f2610%26rft_id%253Dinfo%253Apmid%252F23704171%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/ijlink?linkType=ABST&journalCode=bmj&resid=346/may23_4/f2610&atom=%2Febmed%2F19%2F2%2F68.atom
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Editorial About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.024 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".