Bibliographic record
Abstract
bronchodilators (see SUISSA [1], p. 393, table 1, column 3).Comparison of table 1 in our study [2] and table 1 in SUISSA [1] shows that drug use was more irregular in the Saskatchewan database with low use of the recommended bronchodilators.In spite of this, S. Suissa9s own results using "very first regular exposure identified after diagnosis" in his "conventional intention-to-treat approach" still indicated a significant association between ICS use and mortality, rate ratio 0.75 (0.62-0.90).Furthermore, S. Suissa ignores that our study used two designs, a cohort approach for the main analysis and a nested case-control approach to explore a doseresponse relationship, with both methods indicating an association with ICS.The latter design has been described previously by SUISSA [5] as one that simplifies the cohort analysis when exposures vary over time and leads to valid estimates with negligible loss in precision.Finally, SUISSA [1] used a time-dependent exposure approach and obtained results, which suggested that inhaled corticosteroids were not better than bronchodilators at reducing the risk of death in chronic obstructive pulmonary disease patients.We are not surprised that the benefit of inhaled corticosteroids could not be established with the treatment switching approach.This methodology is known to be valid only if the reason for the switch to inhaled corticosteroids is unrelated to the patient9s subsequent risk of death [6].In our setting, the switch to inhaled corticosteroids was unlikely to be independent of mortality risk.Clinical experience suggests inhaled corticosteroids would be prescribed to sicker patients who were no longer responsive to bronchodilator therapy alone.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.853 | 0.815 |
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".