Frequency of brain ventricular enlargement among patients with diabetes mellitus
Bibliographic record
Abstract
AIMS: To determine the prevalence of dilated ventricles and concomitant high blood glucose measures. METHODS: We retrieved blood glucose measures from the emergency department database and selected a subgroup of individuals having both the radiological marker Evans' index (EI) values and blood glucose measures. RESULTS: Out of 1221 consecutive patients submitted to axial Computed Tomography scans, a blood glucose measure was detected in 841 individuals. 176 scans (21 %) showed an EI > 0.30. According to the blood glucose categorization, diabetic patients were 104 (12 %), 25 of them (24 %) were dilated (mean EI 0.33). The age difference between dilated and not-dilated ventricles is about ten years in not-diabetic participants, whereas it is five years in diabetic participants. The age difference between dilated and not-dilated ventricles is about 10 years in diabetic men, whereas it zero in diabetic women. CONCLUSIONS: Pathological ventricular enlargement is more frequent in men and in the elderly. In diabetic patients (especially women), the cerebral ventricles enlarge faster than in non-diabetic individuals. Age, sex, and diabetes may interact in determining how cerebral ventricle size changes over time, especially in diabetic women, making routine brain imaging advisable in these patients after the age of 70 years.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".