Abstract 188: Comparison of Physician- and Patient-Assessed Impressions of Treatment Efficacy: Insights from the TERISA Clinical Trial
Bibliographic record
Abstract
BACKGROUND: Physicians typically rely on their qualitative interpretation of patients’ symptoms to evaluate the efficacy of antianginal therapy. However, the accuracy of this approach has not been examined. METHODS: Using data from the international multicenter TERISA clinical trial of patients with type 2 diabetes, CAD, and stable angina, we compared the physician’s assessment of change with patient-reported changes in angina. Physicians were asked to complete a well-validated visual analog scale (PGAVAS) at baseline and after 8 weeks of treatment. The PGAVAS asks the physician to rate the disease activity of their patient on a scale of 0 (“very good”) to 100 (“very bad”). A clinically important change in PGAVAS was defined as 8.5 points (i.e., half the baseline SD; a moderate change by Cohen’s effect size). Change from baseline in PGAVAS was compared with change from baseline in angina frequency (AF) as measured by an electronic daily diary. Among patients with an improvement in AF of ≥ 1 weekly episode, multivariable logistic regression examined predictors of physician recognition of change. RESULTS: Among 927 patients in TERISA, 892 (96%) had PGAVAS scores at baseline and at 8 weeks. Of the 605 patients with improvement in AF of ≥ 1 episode, 291 (48%) were recognized by the physician as improved (per the PGAVAS). Comparatively, of the 287 patients with unchanged or worsened AF, 197 (69%) were believed to be unchanged or worse by the physician. Overall, 45% of physicians’ assessments were discordant with their patients’ symptoms. There was a significant but poor correlation between the change in PGAVAS and change in patient-reported AF (Figure; r = 0.15). In multivariable regression, the degree of change in AF was significantly associated with physician recognition of change, with each 1 episode reduction in weekly AF associated with a 12% increased odds of physician recognition (95% CI 1.05-1.20). Age, sex, geography, and baseline AF were not associated with recognition. CONCLUSION: In TERISA, physicians were limited in their ability to recognize significant improvement in patients’ angina, although greater patient change was associated with greater physician recognition. These data suggest that patient-reported outcomes may provide added value to physicians in monitoring their patients’ angina and health status.
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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 | 0.039 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".