Validity of Self-Reported Hypertension: Findings from the Thai Cohort Study Compared to Physician Telephone Interview
Bibliographic record
Abstract
BACKGROUND: Surveys for chronic diseases, and large epidemiological studies of their determinants, often acquire data through self-report since it is feasible and efficient. We examined validity and associations of self-reported hypertension, as verified by physician telephone interview among participants in a large ongoing Thai Cohort Study (TCS). METHODS: The TCS investigates the health-risk transition among distance learning Open University students living all over Thailand. It began in 2005 and at 4-year follow-up, 60 569 self-reported having or not having doctor diagnosed hypertension. Two hundred and forty participants were randomly selected from each of the "hypertension" and "normotension" self-report groups. A Thai physician conducted a structured telephone interview with the sampled participants and classified them as having hypertension or normotension. The sensitivity, specificity, positive and negative predictive value (PPV and NPV) and overall accuracy of self-report were calculated. RESULTS: The sensitivity of self-reported hypertension was 82.4% and the specificity was 70.7%. As true prevalence was simulated to vary from 1% to 50% the overall accuracy of self-report varied little from 71% to 75%. High sensitivity and negative predictive value related to female gender, younger age (?40 years), higher education attainment and not visiting a physician in the last 12 months. High specificity and positive predictive value related to female gender, older age, higher education attainment and visiting a doctor in the previous year. CONCLUSION: Self-report of hypertension had high sensitivity and good overall accuracy. This is acceptable for use in large studies of hypertension, and for estimating its population prevalence to help formulate health policy in Thailand.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".