The Reliever Reliance Test: a pilot study evaluating a pragmatic tool to address SABA over-reliance in a primary care setting
Bibliographic record
Abstract
Background: Over-use and over-reliance on short-acting beta2 agonists (SABA) is associated with poor asthma control and greater risk of exacerbations and death. Identifying and addressing patient beliefs associated with SABA over-reliance is key to reducing over-use. The Reliever Reliance Test (RRT) is a pragmatic, self-test tool designed to identify and address these beliefs. Aim: To assess the feasibility and potential impact of delivering the RRT in a UK primary care setting. Methods: Patients with asthma were identified through two primary care practices in North West London. Patients with potential SABA over-use or uncontrolled asthma were invited to complete the RRT online. Those that completed the RRT were invited to take part in a service evaluation to assess their feedback on the service. Results: Of 108 eligible patients, 94 were sent the RRT. 24 people (26%) completed the RRT. Of these, 13 (26%) were classed as high risk of over-reliance, 8 (33%) at medium risk and 3 (13%) at low risk of over-reliance. 63% of high risk patients said they intended to visit their doctor to discuss their treatment. Of those that completed the service evaluation, 79% said the service was helpful and 64% said it made them feel differently about their SABA. Conclusions: This pilot real-world study demonstrates that the RRT is a feasible tool to use in primary care, with around a quarter of patients using the tool, and almost two thirds intending to change their behaviour as a result. Research is now needed to explore the extent to which it will change behaviour.
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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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".