Medical and Dental Visits of Chronic Kidney Disease-Diagnosed Participants Analyzed From the Specific Health Checkups Results in Japan: TAMA MED Project-CKD
Bibliographic record
Abstract
BACKGROUND: Since 2012, Tama City has promoted the early detection of chronic kidney disease (CKD), through an initiative that measures serum creatinine as part of the specific health checkups. We examined preventive measures against CKD deterioration based on the outcomes of this initiative. METHODS: The complications, medication status, body mass index, smoking status and other determining factors were surveyed among CKD-diagnosed participants over 3 years between 2013 and 2015. Moreover, factors aggravating CKD were investigated via a survey of medical and dental visits based on health insurance claim data over the same period. RESULTS: There was an increased rate of comorbid hypertension with each increase in the CKD stage. Comorbidity rates of diabetes mellitus, dyslipidemia, obesity, and smoking increased until CKD stage G4, and then decreased from stage G5. A substantial number of participants with CKD stage G3b and above were not medicated despite comorbidities like hypertension, diabetes mellitus and dyslipidemia. While the rate of regular visits at medical institutions was seen to increase significantly in accordance with the worsening degree of CKD, there were also individuals who, despite having severe CKD, did not visit medical institutions specializing in internal medicine. The rate of dental visits decreased as the CKD stage increased, and further decreased as the diabetic control status worsened. CONCLUSIONS: CKD patients should become aware of the importance of the dental visit because only a limited number of patients with advanced CKD received dental care.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".