[PP.LB01.14] PREDICTIVE VALUES FOR OBESITY AND DIABETES IN PRIMARY CARE
Bibliographic record
Abstract
Objective: To evaluate the predictive value of blood pressure (BP), age, body mass index (BMI) and pain in predicting obesity and diabetes mellitus (DM) in primary care (PC). Design and method: Cross-sectional study with 496 adult consecutive PC patients was performed. The cardiovascular risk was determined (age, male gender, BP, BMI). Three groups were analysed: the 1st group – healthy patients (N = 97), the 2nd group - patients with obesity (BMI >30) and somatic disease, but not DM (N = 85), the 3rd group - DM patients (N = 22). McGill Pain self-assessment questionnaire was filled in. Information on the clinical diagnoses was obtained from the patients’ medical records. Results: All PC samples did not differ by gender (men 38,1%, 39,1% and 40,9%, resp.), but they differed by age (41 ± 14; 54 ± 14 and 60 ± 9, resp.). Higher systolic BP (p = 0,023), older age (p = 0,002) and presence of pain in legs (p = 0,025) were predictors of DM. Older age (p < 0,001) and pain in legs (p = 0,009), but not high BP were predictors of obesity. When the 1st and the 3rd groups were compared, the greatest differences were found in pain in legs, (1.0% vs. 13.6%, p = 0.02), and in body parts, situated lower than plexus (LP) (4.1% vs. 18.2%, p = 0.038). Results found that older age (p = 0,002), higher systolic BP (p = 0,023) and presence of pain in LP (p = 0,034) were predictors of DM, while predicting obesity, the predictors were older age (p < 0,001), and pain in LP (p = 0,004) only. Conclusions: Pain is valuable marker to predict diabetes next to standard confounding factors such as BP, age and BMI, and more valuable than BP to predict obesity in PC 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.247 | 0.113 |
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".