Assessing Quality of Life and Medical Care in Chronic Angina: An Internet Survey
Bibliographic record
Abstract
BACKGROUND: Angina is a clinical syndrome whose recognition relies heavily on self-report, so its identification can be challenging. Most data come from cohorts identified by physicians and nurses at the point of care; however, current widespread access to the Internet makes identification of community cohorts feasible and offers a complementary picture of angina. OBJECTIVE: To describe a population self-identified as experiencing chronic angina by use of an Internet survey. METHODS: Using email and an Internet portal, we invited individuals with a diagnosis of angina and recent symptoms to complete an Internet survey on treatment and quality of life (QOL). In total, 1147 surveys were received. The main analysis was further limited to those reporting a definite coronary heart disease (CHD) history (N=646, 56% of overall). RESULTS: Overall, about 15% reported daily angina and 40% weekly angina. Those with more frequent angina were younger, more often depressed, and reported a shorter time since diagnosis. They also had substantially worse treatment satisfaction, physical function, and overall QOL. Fewer than 40% were on ≥ 2 anti-anginals, even with daily angina. The subjects without a history of definite CHD had unexpectedly low use of antianginal and evidence-based medicines, suggesting either a lack of specificity in the use of self-reported angina to identify patients with CHD or lack of access to care. CONCLUSIONS: Use of inexpensive electronic tools can identify community-based angina cohorts for clinical research. Limitation to subjects with a definite history of CHD lends diagnostic face validity to the approach; however, other symptomatic individuals are also identified.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".