Switching Inhalers: A Practical Approach to Keep on UR RADAR
Bibliographic record
Abstract
The choice of an inhaler device is often as important as the medication put in it to achieve optimal outcomes for our patients with asthma and/or COPD. With a multitude of drug-device combinations available, optimization of respiratory treatment could well be established by switching devices rather than changing or even augmenting pharmacological or non-pharmacological therapies. Importantly, while notable between-device differences in release mechanism, particle size, drug deposition and required inspiratory flow exist, a patient uncomfortable with their device is unlikely to use it regularly and certainly will not use it properly. Switching requires a careful process and should not be done without patient consent. Switching devices entails several steps that need to be considered, which can be guided using the UR-RADAR mnemonic. It starts with (i) UncontRolled asthma/COPD (or UnaffoRdable device), followed by RADAR: (ii) review the patient's condition (e.g. diagnosis, phenotype, co-morbidities) and address reasons for suboptimal control (e.g. triggers, smoking, non-adherence, poor inhaler technique) to be ruled out before switching; (iii) assess patient's skills related to inhalation (e.g. inspiratory force); (iv) discuss inhaler switch options, patient preferences (e.g. size, daily regimen) and treatment goals; (v) allow patients input and use shared decision-making to decide final treatment choice, acknowledging individual patient skills, preferences and goals; and (vi) re-educate to the new device (at minimum, physical demonstration, verbal explanation and patient repetition, both verbally and physically) and prime the patient for the follow-up (i.e. explain the future patient journey, including multidisciplinary work flows with physicians, nurses and pharmacists).
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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 | 0.023 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.026 | 0.030 |
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".