The role of spirometry in the management of obstructive airways disease: How pharmacists should get involved
Bibliographic record
Abstract
Patient caseYour patient, PR (a 50-year-old man who is an ex-smoker), comes in to renew his short-acting β2-agonist (SABA).His physician has told him he has asthma.He has been getting his SABA from your pharmacy for only the past year or so and has a combined inhaled corticosteroid and long-acting β-agonist (ICS/ LABA) on file.You know that you are supposed to do several things here: assess his asthma control (daytime and nighttime symptoms, exacerbations, etc.), ensure that his inhaler is being used correctly, review other medications he is taking and consider possible triggers.You consider the SABA use in context of his other medications and wonder why he has come in for an inhaler only 1 month after the last one.Does that not mean his asthma may be out of control?Why isn't he filling his ICS/LABA prescription?All these questions are important, but your first step should be to confirm his diagnosis, as choice of appropriate therapy is dependent on an accurate diagnosis.So, your first question is: "Does the patient have asthma, chronic obstructive pulmonary disease (COPD) or another lung problem altogether?"If the patient has never had an objective measure of pulmonary function, perhaps this should be the first step?
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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 | 0.018 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.020 | 0.026 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".