Exacerbations in non-COPD patients: truth or myth—authors’ response
Bibliographic record
Abstract
Exacerbations in non-COPD patients: truth or mythauthors' response Dear Sir, We are grateful to Dr Khurana and Dr Aggarwal for their interest 1 in our paper. 2n their first point, we agree that we should be careful about terminology.We evaluated a random sample of individuals representative of the general population rather than patients, so the findings that episodes of respiratory events occurred in these subjects would likely reflect real events in the general population.Furthermore, our study definition for exacerbations was the same standard questionnaire criteria for exacerbations used in clinical trials of selected patients with COPD. 3 On the second point on aetiologies for non-COPD subgroup in this study, we would like to emphasise that 'non-COPD' was defined by the absence of chronic airflow limitation using the standard, although arbitrary spirometric criteria.We excluded those individuals with selfreported chronic obstructive lung diseases, namely, COPD, chronic bronchitis, emphysema and asthma and evaluated bronchodilator reversibility but did not perform bronchoprovocation test.Thus, residual confounding by undiagnosed asthma or bronchiectasis remains.We could not address other specific aetiologies in this study, and further data would require linkage to administrative databases and longitudinal follow-up of the cohort.On the third point of aetiologies for exacerbations, we do not have aetiologies for the exacerbation-like events.However, to be fair, do we really know the aetiologies for exacerbations among patients with COPD?We assume most of the causes are viral or bacterial infections, but data on causality are lacking.This is an area that requires further exploration.Our study could only address this point indirectly.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.034 | 0.028 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".