Bibliographic record
Abstract
BACKGROUND: Several recent trials in chronic obstructive pulmonary disease (COPD) have assessed the effectiveness of the fluticasone-salmeterol combination inhaler in preventing COPD exacerbations, while finding an increased risk of pneumonia. The number needed to treat (NNT) is a simple measure to perform the comparative benefit-risk impact, but its calculation involving repeated outcome events such as COPD exacerbations has been incorrect. We describe the proper methods to calculate the NNT and, using data from published trials, apply them to evaluate the relative impact of fluticasone-salmeterol treatment on exacerbations and pneumonias in patients with COPD. METHODS: We review the fundamental definition of NNT and quantify it for situations with varying follow-up times. We review the 'event-based' NNT, proposed and used for repeated event outcomes, show its inaccuracy, describe its proper use and provide an approximate formula for its application. RESULTS: We show that a 1-year trial of the fluticasone-salmeterol combination versus salmeterol used the incorrect event-based approach to calculate the NNT as two patients that need to be treated for 1 year to prevent one COPD exacerbation, when the proper calculation results in a NNT of 14. In contrast, 20 patients need to be treated to induce one pneumonia case. For the TORCH trial, the NNT is 44 patients treated for 3 years with fluticasone-salmeterol versus salmeterol to prevent one exacerbation compared with 16 patients to induce one pneumonia case. CONCLUSIONS: The NNT is a useful measure of the effect of drugs, but its proper calculation is essential to prevent misleading clinical practice guidelines.
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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 | 0.031 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".