Helping Patients Follow Prescribed Treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
MEDICAL RESEARCH DURing the past few decades has produced efficacious treatments for many health care disorders and, increasingly, these treatments can be selfadministered. Unfortunately, low adherence can undermine the effectiveness of care at many steps in the process. For example, 49% of patients who demonstrated elevated blood pressure on community screening failed to follow through with a referral for follow-up assessment. Of those who enter the medical care system, more than a third may drop out, especially during the first few months. While in care, the average consumption of medication has been found to be about 50%, with a very wide range from none to substantially more than 100%. Compliance with instructions to lose weight or stop smoking is substantially lower, with long-term success rates less than 10%. One of the important difficulties in managing low adherence is lack of accurate and affordable measures. Clinicians must frequently rely on their own judgment but unfortunately demonstrate no better than chance accuracy in predicting the adherence of their patients, even among patients for whom they feel confident about their predictions. A pragmatic approach to measuring adherence is presented in BOX 1. Based on a systematic review of studies adherence measures, asking nonresponders about their adherence will detect more than 50% of those with low adherence, with a specificity of 87%. Even when patients indicate that they have not taken all their medications as prescribed, their estimates usually substantially overestimate their actual adherence. Thus, the key validated question is “Have you missed any pills in the past week?” and any indication of having missed 1 or more pills signals a problem with low adherence. Overestimation of adherence by patients is difficult to study and is presently poorly documented. Reasons for overestimation could include difficulty recalling the details of medication taking, attempting to please practitioners or to avoid confrontation, or a combination of these factors. Other practical measures to assess adherence include watching for those who do not respond to increments in treatment intensity and patients who fail to attend appointments. More objective measures of compliance can also be of use when available. For example, drug levels in body fluids (blood, saliva, urine) can help in assessing patient compliance (eg, serum digoxin levels and levels of anti-
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 it