Increased cardiovascular risk factors in breast cancer survivors identified by routine measurements of body composition, resting heart rate and arterial blood pressure
Bibliographic record
Abstract
PURPOSE: The main objective of this prospective study was to obtain a better understanding of the body composition and vital sign measures of cancers survivors (CS) when compared to regular (R) patients. METHODS: A total of 9,315 female patients were evaluated: 476 CS and 8,839 R patients. Kinesiologists worked side by side with the medical/oncology team to collect a number of base-line measurements on body composition, resting heart rate, and blood pressure as part of the standard intake evaluation during the female patients' annual checkup. RESULTS: CS were more likely to have a higher BMI (P = 0.001) and a larger waist circumference (P = 0.001) than R patients. CS were also shown to have higher blood pressure values: diastolic pressure of 76.9 mmHg ± 10.5 VS 75.5 mmHg ± 9.9, (P = 0.01) and systolic pressure of 129.8 mmHg ± 17.2 VS 126.7 mmHg ±17.4 (P = 0.001) compared to R patients, respectively. Regression analysis looking at the relationship between mean arterial pressure and waist circumference did not show any difference between the two groups (CS vs R). CONCLUSION: CS who had a higher BMI, a larger waist circumference and higher blood pressure levels, are probably at greater risk for developing cardiovascular disease, diabetes, various musculoskeletal problems as well as an increased risk for various forms of cancers including reoccurrence of previously treated cancer when compared to R patients. Changes in body composition should be considered by the medical team when looking at preventative healthcare strategies for their CS 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".