Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a common disease leading to further morbidity and significant mortality. The first step for any condition is to make the appropriate diagnosis, and spirometry barriers abound in practice around the world. It is tempting to undertake mass screening on all smokers to detect COPD. While this would pick up cases of COPD, results of studies of its effect on COPD end-points such as exacerbations, hospitalisations and mortality are disappointing. As such, aggressive case finding of COPD by screening for symptoms that patients may not themselves perceive is very important in primary care, with subsequent spirometry defining the diagnosis.We also have to separate out population screening from individual patient interactions. Performing spirometry, even on a truly asymptomatic patient, may allow earlier diagnosis and modification of risk factors such as smoking (mostly) and exacerbation risk. It also recognises patients with early disease who are at high risk of comorbidities such as cardiac illness, such that appropriate treatment strategies can be implemented. Making a diagnosis, and even the fact of worrying about such a diagnosis, can affect the motivational level of the individual patient to cease smoking; all patients should of course be counselled to stop smoking. As such, consider the individual patient in front of you for unrecognised symptoms and therefore unrecognised illness, as making a diagnosis earlier can allow the institution of care, including smoking cessation, vaccination, bronchodilators and comorbidity management.
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 | 0.110 | 0.282 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.016 | 0.029 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.010 | 0.022 |
| Insufficient payload (model declined to judge) | 0.008 | 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".