Bibliographic record
Abstract
The decline in lung function over time is a fundamental measure of disease progression among patients with chronic obstructive pulmonary disease (COPD). As a result, forced expiratory volume in one second (FEV1) decline has been used as a central outcome measure for many randomised controlled trials evaluating whether pharmacological treatments could modify the natural history of COPD. These trials, mostly focussed on assessing the benefit of inhaled corticosteroids, and their meta-analyses highlighted the complexities of analysing data from repeated FEV1 measurements over time, particularly in the context of studying COPD patients who discontinue follow-up early and in large numbers. This has lead to contradictory results and divergent conclusions 1–3. At present, the recent paper from the Towards a Revolution in COPD Health (TORCH) trial reports that the yearly decline in FEV1 was significantly slower with fluticasone, salmeterol or both compared with placebo 4. A common misconception in the interpretation of these studies is that the effect of the study drug on FEV1 decline is likely to be greater than the data suggest. The rationale is that more patients receiving placebo were discontinuing the study drugs and the patients who dropped out early had a steeper decline in lung function than those who remained. Consequently, it has generally been believed that the resulting analysis “actually minimises the differences observed in the rate of FEV1 decline” 4. To clarify this misconception, a fundamental aspect of the design and statistical analysis of these trials is addressed, namely bias from regression to the mean resulting from the absence of an authentic intent-to-treat approach. It is described based on the TORCH trial and illustrated using data from the Canadian Optimal randomised trial. Most of the trials to date are not designed for a full intent-to-treat …
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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 | 0.428 | 0.583 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".