Bibliographic record
Abstract
Recent trials reported significant reductions in all-cause mortality with single-inhaler triple therapy for chronic obstructive pulmonary disease (COPD). However, reviews of these trials identified inconsistencies in the findings and methodological issues with the design and analysis, including the "adverse impact of inhaled corticosteroid (ICS) withdrawal rather than the addition" of the triple therapy. Indeed, ICS were discontinued in over 70% of the patients in these trials and 40% already using triple therapy, muddying the interpretation of the data. The "adaptive" clinical trial design is an efficient approach that allows continual modification of the study treatment allocation during follow-up. In this article, we propose the "adaptive selection" trial design, which applies the adaptive concept to the selection of patients into the trial by adapting the randomization choices to the treatment already used by the patients. With such a design, patients already on triple therapy would be excluded outright from trials of triple therapy effectiveness, while the others are randomly allocated to specific treatment arms according to their current treatment, avoiding issues of treatment withdrawal effects. Adaptive selection trials should be the norm for studies of COPD therapies. This approach would avoid the vexing effects of treatment withdrawal that have afflicted the recent triple therapy trials. This concept of adaptive selection has been applied in COPD to the question of whether patients can be safely de-escalated from ICS. It is time to also apply it to studies of the effectiveness of treatment escalation.
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 | 0.229 | 0.353 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".