Blood pressure assessment in severe obesity: Validation of a forearm approach
Bibliographic record
Abstract
OBJECTIVE: Obesity is frequently associated with systemic hypertension. Blood pressure measure is inaccurate in severely obese patients because of poor cuff size fitting. The aim of the study is to assess the degree of agreement between the intra-arterial method as the gold standard vs. noninvasive methods, i.e., forearm blood pressure and upper-arm blood pressure measures. DESIGN AND METHODS: A total of 1285 measures of intra-arterial and forearm blood pressure were taken in 51 severely obese patients in a supine position in the operating and the recovery room. A subset of 352 upper-arm measures were taken in the recovery room and compared to the intra-arterial and the forearm methods. RESULTS: Correlation between the intra-arterial and the forearm measures was 0.90 (P < 0.001) for the 2570 data (systolic and diastolic). Compared to intra-arterial, the forearm method overestimated systolic (6 ± 16 mm Hg, P < 0.001) and underestimated diastolic blood pressure (2 ± 11 mm Hg, P = 0.03). Compared to intra-arterial, upper-arm underestimated systolic (8 ± 16 mm Hg, P < 0.01) and overestimated diastolic blood pressure (9 ± 7 mm Hg, P < 0.001). CONCLUSION: The magnitude of differences between the intra-arterial and forearm method was less than differences between the intra-arterial and upper-arm method. Our results suggest that forearm method may be a more accurate alternative to upper-arm measurement to assess blood pressure in severely obese 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".