The GOLD 2023 proposed taxonomy: a new tool to determine COPD etiotypes
Bibliographic record
Abstract
The population burden of COPD remains high well into the 21st century, although with great regional variability [1]. Changes in the geographical and secular distribution of COPD points to the interaction of a number of risk factors beyond tobacco and other forms of smoking, which is still considered the first and foremost causal risk factor of COPD. We read with interest the new Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2023 document, proposing a new taxonomy of COPD and suggesting six etiotypes on the origin of COPD [2]. This taxonomy is based on work found elsewhere [3, 4]. To the best of our knowledge, these etiotypes are not evidence-based, and the information required to obtain them has not been explored. We aimed to identify the information required to determine the classification of COPD etiotypes by GOLD 2023, by means of panel discussion and consensus by the authors. The information required to determine the classification of COPD etiotypes by GOLD 2023 is hard to obtain https://bit.ly/43ZJhCc All authors are members of the COPD Cohorts Collaborative International Assessment (3CIAplus) consortium, and aimed to determine COPD etiotypes in their pooled database.
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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 | 0.029 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.026 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".