Sample sizes for clinical trials using sputum eosinophils as a primary outcome
Bibliographic record
Abstract
Clinical trials do not report sputum eosinophil data in a consistent method and this makes it difficult to compare across studies and to evaluate the sample sizes estimated in these studies. The objectives of the paper are: 1) to systematically review reporting of effect size and sample calculations in randomised controlled trials using sputum eosinophil count as a primary outcome and 2) to illustrate sample size estimation under different methods of data representation using data from an effective anti-eosinophil treatment strategy (mepolizumab). Randomised controlled trials in adults (excluding allergen provocation models) of treatment of asthma and chronic obstructive pulmonary disease for the past 10 years were searched in Ovid MEDLINE and 20 studies were identified that met all the inclusion criteria. Only nine studies discussed sample size calculation. Change from baseline was used as an outcome in 11 studies and was expressed as change in absolute percentage count, percentage change from baseline or as fold changes. Assuming a minimal clinically important reduction of 15% in absolute terms, 18 subjects in each arm will be required to achieve 80% power using an ANCOVA analysis, which we recommend, to detect significance with an alpha error of 0.05.
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.
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 | 0.288 | 0.642 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.019 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".