Should COPD stand for “comorbidity-related obstructive pulmonary disease”?
Bibliographic record
Abstract
The burden of chronic obstructive pulmonary disease (COPD) is enormous and growing. Currently, COPD affects 300 million people worldwide and kills more than 3 million people each year [1]. COPD exacts a large financial toll on society because it is the leading cause of hospitalisation in many jurisdictions in the Western world. In the USA, for example, which has a very robust population-based data, COPD accounts for 650 000 hospital admissions and 1.7 million emergency visits per year [2]. Because hospital-based care is very expensive, the costs of COPD care are staggering, estimated to be $101 billion per year in the USA [3]. These costs are expected to double over the next 10 years, owing largely to the ageing population. Regrettably, the current treatments for COPD are suboptimal and woefully inadequate to reduce the burden of COPD-related hospitalisations. Indeed, even with the “best” available therapies, one in 10 patients hospitalised with an acute exacerbation will die during their hospital stay and even among the “lucky” ones who survive, a majority will experience adverse effects related to corticosteroids, including hyperglycaemia, insomnia, hypertension and weight gain, and one in three patients will relapse, requiring another hospitalisation within 6 months of discharge [4]. These observations beg the question: why are COPD hospitalisations so difficult to treat and manage, and why are they associated with such poor health outcomes? There is a pressing need to diagnose and effectively treat comorbidities of COPD
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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 | 0.008 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.005 | 0.013 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".