The T-Connector Approach: a simultaneous method for in-office assessment of home blood pressure monitor accuracy
Bibliographic record
Abstract
Context Hypertension Canada recommends home blood pressure monitors (HBPM) for diagnosing and managing hypertension. However, 29% of HBPMs are inaccurate by 10 mmHg or more. This makes determining HBPM accuracy of great clinical importance, but there is no established method of doing so in the office. Serial blood pressure (BP) measurements can be attempted, but a large number is needed to rule out transient changes in BP. Objective To introduce the novel approach of using a T-Connector between a patient’s HBPM and their physician’s office sphygmomanometer to permit a simultaneous blood pressure reading. Study Design 89 participants had the accuracy of their personal HBPMs assessed by 1) a family physician using our T-Connector Approach and 2) the gold standard method called Dual Observer Auscultation, which involves 2 physicians alternating between the HBPM and a mercury manometer for 9 readings. Setting Kaye Edmonton Clinic Family Medicine Clinic and a hypertension research lab. Population Adults who own a HBPM. Instrument The T-Connector Approach uses latex tubing, a plastic barbed tee piece, and a metal connector from the primary care clinic (the piece used to switch blood pressure cuffs) to connect a patient’s HBPM to their physician’s office sphygmomanometer. Outcome Measures an inaccurate HBPM was defined as providing a blood pressure reading ≥ 10 mmHg different than the T-Connector or dual observer auscultation methods. We primarily compared the ability of the T-Connector to detect inaccurate monitors compared to the dual observer auscultation (DOA) gold standard method. Results Compared to DOA, the T-Connector’s assessment of HBPM error was an average of 2.97 mmHg lower, 95% limits of agreement were -10.72 to 16.65 mmHg, and the intraclass correlation coefficient (ICC) was 0.47. Sensitivity and specificity for detecting an inaccurate monitor were 0.33 and 0.97 respectively, with a positive predictive value (PPV) of 0.75, and a negative predictive value (NPV) of 0.85. Cohen’s kappa was 0.39. Conclusion Based on the Cohen’s kappa of 0.39 (fair agreement), PPV of 0.75, and NPV of 0.85 there appears to be reasonable utility for using a T-Connector to qualitatively declare a HBPM to be inaccurate for its user. However, the sensitivity was low (0.33) and the ICC being <0.5 indicates poor agreement for quantitative assessment of the mmHg of error.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".