Adherence with epinephrine autoinjector prescriptions in primary care
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to estimate primary adherence for epinephrine autoinjector (EA) prescriptions in primary care practices in Manitoba, Canada. METHODS: A retrospective analysis of electronic medical record and administrative data was performed to determine primary adherence, defined as dispensation of a new EA prescription within 90 days of the date the prescription was written. Multivariable logistic regression models were used to test predictors of filling an EA prescription. RESULTS: Of 1212 EA prescriptions written between 2012 and 2014, only 69.9% (N = 847) were filled. An increased number of prescriptions for non-EA mediations was associated with an increased odds ratio of not filling an EA prescription. INTERPRETATION: This is the first study in Canada to examine adherence for EA prescriptions. The non-adherence rate identified is higher than rates previously reported in the literature, and indicates that many EA prescriptions for adults seen in primary care may never be filled. It also suggests that prescriptions of EAs for all patients at risk of anaphylaxis in community settings should consistently be accompanied by concise information about the importance of having the EA prescription filled and having the EA readily available.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".