Assessing and improving patient knowledge gaps relating to the administration of elexacaftor/tezacaftor/ivacaftor in an adult cystic fibrosis clinic population
Bibliographic record
Abstract
Background: Elexacaftor/tezacaftor/ivacaftor (ETI), (trade name Trikafta®), is a novel drug for cystic fibrosis (CF). It combines a CFTR potentiator (ivacaftor) with 2 correctors (tezacaftor and elexacaftor). One study estimated ETI will increase life expectancy of CF patients from 37 to 83 years [1]. Despite the benefits, ETI has a complex dosing schedule [2]. Aims: The purpose of this project was to evaluate and improve patient understanding of ETI. Methods: All patients taking ETI at an adult CF clinic were invited to take a 20 question survey on dose, interactions, contraindications, protocol for missed doses, and patient resources. After the survey, the physician reviewed the answers with the patient, discussed incorrect answers, provided the manufacturer’s prescribing information, and distributed information on medication administration and avialable resources. A 2nd survey was administered 1 year later followed by the same protocol. A paired T-test was used to compare the differences between test results. Results: Forty-eight patients completed the initial survey, with an average score of 69.1%. The most frequently missed questions included patient resources, followed by drug interactions. Alarmingly, 58% missed at least 1 of 3 questions pertaining to procedures for missed doses. At the time of this article, 21 patients had completed both surveys. Mean scores were 63% and 75% (difference 12%; 95% CI 3.58 - 20.4; P=0.0075). Conclusions: Significant patient knowledge gaps exist regarding ETI. A simple educational intervention may provide lasting improvements in patient knowledge relating to ETI administration.
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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 | 0.009 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".