Factors Associated With the Frequency of Medical Consultations in Patients With Various Types of Diabetes Mellitus
Bibliographic record
Abstract
Background: Proper management of diabetes mellitus (DM) is essential to prevent long-term complications, improve patients’ quality of life, and reduce the economic burden on healthcare services. The aim of this study was to determine the factors associated with the number of medical consultations received in the last quarter by patients with different types of diabetes, who were affiliated with the Comprehensive Health Insurance (SIS: acronym in Spanish) in Peru. Methods: A cross-sectional analysis of the database of patients with DM affiliated with SIS in Peru was conducted. Two robust variance regression models were used to identify potential associated factors. Results: Data from 1,355,354 patients were analyzed. In model 1, which included comorbidities as separate variables, it was found that men, older individuals (especially those aged 60 - 69), and residents of the jungle region had a higher probability of receiving more medical consultations. The presence of obesity/dyslipidemia, hypertension, and mental health disorders also increased the likelihood of more consultations. In comparison, patients with type 2 DM had fewer consultations compared to those with type 1 DM. The findings were consistent with the first model in model 2, which included the total number of comorbidities instead of each separately. Additionally, a higher total number of comorbidities was associated with an increased number of medical consultations. Conclusions: Several vital factors influencing the frequency of medical consultations received by DM patients have been identified. Adapting healthcare services to address regional disparities in access to and use of medical services is crucial.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 |
| 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".