The Prevalence of Diagnosed Cutaneous Manifestations during Ambulatory Diabetes Visits in the United States, 1998–2002
Bibliographic record
Abstract
BACKGROUND: The prevalence of diabetes has been rapidly increasing. Previous reports indicated that diabetics are prone to certain cutaneous diseases. OBJECTIVE: To determine the frequencies of diagnosed skin conditions during ambulatory diabetes visits in the USA. METHODS: We evaluated two national ambulatory medical care surveys between 1998 and 2002 and compared the diagnoses of 7 categories of skin conditions in diabetics (n = 9,626) to patients with hypertension (n = 15,997) or gastroesophageal reflux disease (GERD; n = 2,362) using chi2 tests and multivariate logistic regressions. RESULTS: Diabetics were prone to chronic skin ulcers (odds ratio = 62.5, 95% confidence interval = 3.95-989 compared to GERD; 9.97, 6.34-15.7 compared to hypertension), bacterial skin infections (5.95, 2.86-12.4 compared to GERD; 5.15, 3.74-7.08 compared to hypertension) and fungal skin infections (2.66, 1.15-6.16 compared to GERD; 1.99, 1.32-3.01 compared to hypertension) but not to other skin conditions. These findings remained true during primary care physician visits. CONCLUSION: Chronic skin ulcers, bacterial and fungal skin infections are more frequently diagnosed in diabetics. We could not verify that other skin conditions are associated with diabetes, in part due to potential underdiagnosis and underreporting.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".