User‐only design to assess drug effectiveness in clinical practice: application to bisphosphonates and secondary prevention of fractures
Bibliographic record
Abstract
PURPOSE: Different strategies applicable to control for confounding by indication in observational studies were compared in a large population-based study regarding the effect of bisphosphonates (BPs) for secondary prevention of fractures. METHODS: The cohort was drawn from healthcare utilization databases of 13 Italian territorial units. Patients aged 55 years or more who were hospitalized for fracture during 2003-2005 entered into the cohort. A nested case-control design was used to compare BPs use in cohort members who did (cases) and who did not experience (controls) a new fracture until 2007 (outcome). Three designs were employed: conventional-matching (D1 ), propensity score-matching (D2 ), and user-only (D3 ) designs. They differed for (i) cohort composition, restricted to patients who received BPs straight after cohort entry (D3 ); (ii) using propensity score for case-control matching (D2 ); and (iii) compared groups of BPs users versus no users (D1 and D2 ) and long-term versus short-term users (D3 ). RESULTS: Bisphosphonate users had odds ratios (95% confidence interval) of 1.20 (1.01 to 1.44) and 0.95 (0.74 to 1.24) by applying D1 and D2 designs, respectively. Statistical evidence that long-term BPs use protects the outcome onset with respect to short-term use was observed for user-only design (D3 ) being the corresponding odds ratio (95% confidence interval) 0.64 (0.44 to 0.93). CONCLUSIONS: User-only design yielded closer results to those seen in RCTs. This approach is one possible strategy to account for confounding by indication.
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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 | 0.148 | 0.187 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.001 |
| 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".