Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) continues to be associated with increased morbidity and mortality risk in spite of updated guidelines and a better understanding of this condition. Progressive airflow limitation and resultant hyperinflation-the respiratory hallmarks of this complex and often under-diagnosed disease-can be treated with pharmacotherapies emitted via nebulizers, pressurized metered-dose inhalers, dry powder inhalers, or a Soft Mist inhaler. Pharmaceutical company proprietary issues, technological innovations, and societal pressure have expanded the list of available inhalers, with a limited range of medications available for any one device. Each device has different operating and maintenance instructions, and successful use of a given drug/device combination requires that patients understand, maintain, and use each of their devices properly in order to ensure consistent and optimal pulmonary drug delivery. Clinicians are faced with a range of physical and psychosocial issues unique to each patient with COPD that must be overcome in order to match a suitable inhaler to the individual. Improved drug delivery afforded by next-generation inhalers, coupled with an awareness of device-specific and patient-specific variables affecting inhaler use, may improve clinical outcomes in the treatment of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.008 | 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".