Accuracy of Telemedicine in Detecting Uncontrolled Hypertension and Its Impact on Patient Management
Bibliographic record
Abstract
This study was aimed at assessing the diagnostic accuracy of telemedicine among hypertensive patients. This was a cross-sectional analysis of patients attending a hypertension clinic over a year-long study. Patients were seen both by telemedicine and in-person on the same day with order of the encounters randomly determined. A telemedicine system, which utilized phone lines, was employed. For each type of encounter, whether telemedicine (TM) or in-person (IP), clinical data on blood pressure (BP) control as well as physician ordering patterns were collected. Receiver Operator Characteristic (ROC) curves were used to assess the validity of TM as compared to IP in the assessment of uncontrolled hypertension. Sixty-two patients participated resulting in 107-paired visits over the year-long study period. The mean age of the 62 participants was 67.1 +/- 11.4 years; 56.6% were men. ROC curves for detecting elevated mean blood pressure provided an area under the curve (auc) of 0.87 (95% CI, 0.80-0.95). ROC curves for the detection of uncontrolled systolic hypertension provided an auc of 0.86 (95% CI, 0.78-0.93). Telemedicine-determined BP differed slightly, but statistically significant (p < 0.05), from IP assessments. Meanwhile, there was no difference in ordering diagnostic tests or therapeutics detectable between the two encounter types. Telemedicine proved to be a valid means for detecting uncontrolled BP among hypertensive patients.
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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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".