Patient-mediated scale-up of guideline implementation in primary-care: an open-label trial
Bibliographic record
Abstract
To advance the knowledge on how to amplify the impact of effective clinical interventions to the population level, we tested an approach that took the traditional model of care delivery, which relied on busy physician practices to initiate treatment and transformed it into a patient-driven model, enabled by web technology and strategic labor distribution among patient, physician and pharmacist. Varenicline and bupropion are effective pharmacotherapies for smoking cessation, but many clinicians do not proactively discuss these options with their smoker patients. Using cost-free medication as an incentive, our objective was to demonstrate the feasibility of enrolling smokers via the internet in a protocol to engage their family physician in a discussion of smoking cessation treatment with pharmacotherapy. Participants visited the study website, provided consent and completed an on-line assessment. Eligible participants received a personalized script to take to their physician, who had the option of prescribing bupropion or varenicline for 12 weeks. Signed scripts were faxed by the physician's office to a central pharmacy that couriered the medication to the patient. Pharmacist provided one session of brief telephone counseling. In 2 months, 893 participants enrolled, of whom 524 (59%) visited 419 different physicians to have the script signed and faxed (265 varenicline, 259 bupropion). All medication packages were delivered, with only 1.0% (n = 5) requiring address clarification or reshipment. At 6-month follow-up, the 7-day point prevalence of abstinence was 30.3% for varenicline vs. 24.3% for bupropion (p >0.1; OR (95%CI): 1.4 (0.8-2.2)). These quit rates are comparable to those seen in randomized clinical trials. The e-patient movement with the world-wide-web as its forum has patients acting as drivers of their healthcare. This emerging characteristic of population behavior provides a new opportunity for rapid scaling-up of implementation of evidence to the state-or national-level that is otherwise extended to limited practice settings. This study was funded by the Ontario Ministry of Health and Long-Term Care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".