GP survey on how to incorporate LABA/LAMA fixed dose combinations (FDC) in their COPD patients
Bibliographic record
Abstract
Aim: To determine Canadian GPs view on which COPD patients: a) are not optimally treated b) should be treated with LABA/LAMA FDC. Methods: 45 1-hour, 1-on-1 interviews were conducted in Montreal, Toronto and Vancouver. GPs discussed their treatment algorithm for COPD, reviewed how they perceived the importance of ICS in COPD, and which patients would benefit from a LABA/LAMA FDC. Results: GPs suspect COPD in smokers and ex-smokers and classify based on patient symptoms and progression over ensuing visits. They ideally confirm with a spirometer test, especially to differentiate from asthma. This is often omitted by Toronto MDs, citing recent removal of government remuneration cf. Vancouver MDs. Overall a very symptomatic patient or one with frequent exacerbations is considered severe and a patient with no symptoms is mild. Mild patients are often not treated. The most common first line maintenance therapy prescribed is a LAMA. Patients with persistent complaints will receive a LABA/ICS FDC, even if they do not exacerbate. GPs consider the ideal candidate for a LABA/LAMA FDC to be a symptomatic COPD patient despite maintenance therapies, either LAMA or LABA ICS FDC. They expect that this treatment will reduce exacerbations. This could be an opportune time to reconsider the patients on a LABA/ICS FDC, recognizing that many patients may not actually need an ICS. They estimate ∼65%-70% of their patients are on triple therapy (LAMA+LABA/ICS) regardless of exacerbation status. ∼50% of the physicians mention risk of pneumonia with ICS. Mention was also made that a patient with osteoporosis, glaucoma and diabetes could benefit from an ICS-free treatment such as LAMA/LABA FDC.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".