Impact of applying different resting blood pressure cut-points to clear for maximal exercise
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to examine the impact of applying six commonly-used and two proposed resting blood pressure (BP) cut-points to clear individuals for maximal exercise in non-clinical health, wellness, commercial fitness agencies and physically demanding occupation test sites. METHODS: Participants (n = 1670) completed the Physical Activity Readiness Questionnaire for Everyone (PAR-Q+) and had their resting BP measured. Individuals with a BP >160/90 mmHg were further screened for contraindications to exercise using the ePARMed-X+ (www.eparmedx.com), all 1670 were cleared. There were no adverse events during or post exercise. RESULTS: The percentages of participants cleared for each BP cut-point were: <130/80 mmHg (85.3%), <140/90 mmHg (93.4%), <144/90 mmHg (94.6%), <144/94 mmHg (96.3%), <150/100 mmHg (98.6%), <160/90 mmHg (95.6%), <160/94 mmHg (97.8%) and <160/100 mmHg (99.5%). Individuals who would not have been cleared without further screening were significantly older, had a higher BMI, or had a lower maximal oxygen consumption. CONCLUSIONS: Conservative or lower resting BP cut-points currently applied to clear individuals for maximal exercise provide an unnecessary barrier. For individuals categorized as low-to- moderate risk by evidence-based screening tools such as the PAR-Q+ and ePARmed-X+, we recommend a resting BP cut-point of <160/94 mmHg to clear for maximal exercise until sufficient evidence is amassed to support the increase to <160/100 mmHg.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".