Diagnosis and treatment considerations for women with COPD
Bibliographic record
Abstract
The worldwide prevalence of chronic obstructive pulmonary disease (COPD) is growing faster in women than in men. Over the past two decades, COPD-related mortality rates have also grown faster in women, and since the year 2000 more women than men have died from COPD. The greater prevalence of COPD and related mortality reported for men in earlier epidemiological studies may be due to under-diagnosis of women. In addition, factors such as prevalence of symptoms, triggering stimuli, response to treatment, susceptibility to smoking, frequency of exacerbations, impairment in quality of life response to oxygen therapy, presence of malnutrition, airway hyper-responsiveness and depression are more frequently seen in women with COPD. Despite these differences, the current guidelines for the diagnosis and treatment of men or women with COPD are the same. It is important for healthcare professionals to recognise the gender differences in patients with COPD to optimise assessment, monitoring and treatment of this disease. This article reviews the clinical differences between men and women with COPD, current treatment guidelines and its implications for improvement in the management of women with COPD.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".