Why this work is in the frame
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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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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 it