Systematic error in the determination of nocturnal blood pressure dipping status by ambulatory blood pressure monitoring
Bibliographic record
Abstract
BACKGROUND: Vertical displacement of the arm relative to the heart causes inverse changes in blood pressure of approximately 0.8 mmHg for every centimetre change in arm position. Therefore a potential confounding issue in assessing diurnal variation in blood pressure during ambulatory blood pressure monitoring (ABPM) is arm position during sleep. An increase in the number of patients with 'excessive' nocturnal dipping (> 20% decrease in night/day blood pressure) was observed following the creation of an instructional videotape in which patients were advised to muffle the noise of the monitor with a pillow at night. This raised the possibility that patients were placing their arm on top of the pillow reducing nocturnal blood pressure readings. DESIGN: Ambulatory blood pressure monitoring data from 184 patients prior to and from 193 patients following specific instructions not to put their arm on top of the pillow was examined. RESULTS: Following the instructions, the percentage of patients with 'excessive' nocturnal dipping in blood pressure decreased (excessive systolic dipping 17.4 versus 8.8%, P = 0.014; excessive diastolic dipping 37 versus 24.4%, P = 0.01). Consistent with an increase in the ratio of nocturnal/day pressures, there was an increase in the percentage of patients with inadequate nocturnal dipping (< 10% decrease in night/day blood pressure; systolic dipping 33.7 versus 45.6%, P = 0.02; diastolic dipping 13.0 versus 31.6%, P < 0.001) CONCLUSION: Instructing patients to avoid resting their arm on a pillow at night has a substantial effect on the classification of nocturnal dipping status. Patients need clear instructions not to place their arm on a pillow at night during blood pressure monitoring.
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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 | 0.017 | 0.067 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".