Optimizing dose prediction: Weighing MDIs to accurately estimate remaining doses
Bibliographic record
Abstract
Rationale Many metered dose inhalers (MDIs) currently do not have integrated dose counters and patients and healthcare providers in Canada do not have an accurate way to predict the number of remaining doses in a used MDI.Objective The objective of this study was to create equations that would convert weight of commonly used MDI and accurately predict the number of remaining doses in each inhaler and when the inhalers were functionally empty.Methods We weighed Teva-Salbutamol, Atrovent (Ipratropium), Alvesco (Ciclesonide), Flovent (Fluticasone) 125 mcg and 250 mcg, QVAR (Beclomethasone) 50 mcg and 100 mcg and Breztri (Budesonide, Glycopyrronium, Formoterol) MDIs after releasing every 2 doses and analyzed the data using a regression line. We aimed to validate the predictive accuracy of the equations for Teva-salbutamol.Main Results There was a near perfect correlation between weight and remaining doses for all 6 inhalers. The equations were validated and found to be accurate for Teva-salbutamol indicating our methods were sound and accurately predicted the number of remaining doses, when an MDI is functionally empty.Conclusions Equations using inhaler weight can be used to accurately predict remaining doses and to determine when the inhalers are functionally empty for the studied MDIs available in Canada. The equations can be used to prevent the use of empty inhalers and minimize medication waste.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".