Pain and its predictive value for obesity and diabetes in primary care
Bibliographic record
Abstract
The aim was to determine how often patients who come to primary health institution feel pain and if this pain can be associated with obesity and diabetes. The sample consisted of 496 randomly selected primary health care (PHC) adult patients, who agreed to participate in the study and have signed an informed consent form. Information about the patient's disease was collected from records in the patient's medical card. Patients were divided into 3 groups: first –a group of healthy persons who came to the PHC for administrative reasons, the second – non-diabetic inpatients with obesity and somatic diseases, and the third – diabetic patient group. Pain was evaluated using the McGill questionnaire. Cardiovascular risk factors were evaluated (age, gender, blood pressure, body mass index, the presence of diabetes). The results showed that obese PHC individuals (n=85) compared to the healthy persons (coming to the clinic for certificates or who had accompanied their relatives, n=97) are more likely to experience pain throughout the body, except the head and neck area, and the mode of pain is aching, pressing and frustrating –consumptive. Patients with diabetes (n=22) more than healthy persons feel pain in parts of the body, located below the solar plexus (p=0.38), especially in the legs (p=0.022). The logistic regression analysis (Froward Wald) was performed to determine whether the pain in the legs allows predicting a person's obesity or presence of diabetes; and as additional variables were included blood pressure, age, and gender. The results show that with increasing age (p=0.002), systolic blood pressure (p=0.023) and pain in the legs (p=0.025) predict diabetes Predictors (Negelkerke R²=0.488, percentage of correct classification 84.9%). Similarly, older age (p<0.001) and pain Fin the legs (p=0.009) allows to predict whether a person will be obese (Negelkerke R²=0.304, percentage of correct classification 70.9%). PHC patients often feel pain. [...].
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".