Weighing for accuracy: Reducing inhaler waste and misclassification in a pulmonary function clinic
Bibliographic record
Abstract
INTRODUCTION In Canadian pulmonary function testing (PFT) labs, pressurized metered dose inhalers (MDIs) are used to assess airway reversibility, but most lack integrated dose counters. This can lead to continued use of functionally empty inhalers or premature disposal of those with usable doses. Both pose risks: false-negative test results and medication waste. We implemented a weight-based tracking protocol to optimize inhaler use and minimize misclassification.METHODS A prospective quality improvement intervention was conducted in a high-volume PFT clinic. Respiratory therapists used validated equations to estimate remaining doses by weighing salbutamol MDIs. Data were collected over 2 5-week periods: before and after implementing the protocol. The primary outcome was inhaler overuse, which is defined as usage beyond the labeled dose count. Secondary outcomes included premature disposal (wasteful use), extended use, and patients per inhaler.RESULTS Prior to the intervention, 1 of 3 sampled inhalers was overused, with 28 doses beyond depletion. Post-intervention, all inhalers (n = 14) assessed in the post-intervention period were not overused. No inhalers were used past their labeled capacity, indicating complete adherence to dose limits. Additionally, 54 extra days of inhaler use were achieved through safe extension, equivalent to saving 11 inhalers. While inhalers were not used for more patients overall, the protocol significantly improved safety and reliability in dosing, reducing misclassification risk.CONCLUSION A simple weight-based inhaler tracking protocol effectively eliminated overuse of MDIs in a PFT clinic. This low-cost, scalable intervention enhances diagnostic accuracy, reduces environmental impact, and supports medication stewardship in respiratory care settings.
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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 | 0.086 | 0.162 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".