Comparing Medications in a Therapeutic Area Using an NNT Model
Bibliographic record
Abstract
OBJECTIVES: Clinicians are told to use the number needed to treat (NNT) to compare the benefits of therapeutic strategies, and researchers are asked to report results this way, generally without considering differences among the studies from which these were derived. METHODS: The crude NNT currently advocated is compared to the NNT standardized for a common outcome, follow-up time, study population and comparator. An NNT model for cardiovascular disease is described as an example that addresses differences among studies of secondary prevention of cardiovascular disease. Crude NNTs are compared to those obtained from the model. RESULTS: Follow-up in the 18 trials identified varied from 1.0 to 6.2 years; rates of cardiovascular events in the untreated subgroups ranged from 4.8% to 45.9%. The crude NNTs were more variable (9.1-163.7) than those obtained from the model (9.1-75.2). The effect of standardization was substantial in some cases, with proportional changes ranging from a 91% decrease to a 223% increase. CONCLUSION: Using an NNT model to account for differences in study design allows for more meaningful comparisons.
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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 | 0.237 | 0.283 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| 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".