Response to Comment on Suissa. Lower Risk of Death With SGLT2 Inhibitors in Observational Studies: Real or Bias? Diabetes Care 2018;41:6–10
Bibliographic record
Abstract
We thank the authors of the two letters (1,2) for clarifications regarding the design of their observational studies reporting that use of sodium–glucose cotransporter 2 inhibitors (SGLT2i) in type 2 diabetes was associated with over 50% lower rates of all-cause mortality. Thuresson et al. (1) suggest that immortal time bias was essentially absent in the study design of the Comparative Effectiveness of Cardiovascular Outcomes in New Users of SGLT-2 Inhibitors (CVD-REAL) study. I must disagree, particularly in view of some inconsistencies I found between key information provided in the letter (1) and that presented in the study report (3). In that article, the cohort is described as new users of an SGLT2i between November 2012 and November 2016 who were matched on the propensity score to new users of another glucose-lowering drug (oGLD) during that period (3). New users were defined by the first prescription for an SGLT2i or oGLD with no issued prescriptions of that medicine …
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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 | 0.010 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.053 | 0.045 |
| Insufficient payload (model declined to judge) | 0.009 | 0.014 |
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".