A considerable proportion of metabolic syndrome–free adults from Bratislava Region, Slovakia, display an increased cardiometabolic burden
Bibliographic record
Abstract
Although the dichotomous classification of metabolic syndrome (MS) enables the classification of individuals as MS-free or presenting MS, it is inconvenient for assessing cardiometabolic risk in MS-free individuals. Continuous MS score allows for estimation of cardiometabolic burden even in MS-free subjects. We used the scores to estimate the proportion of MS-free subjects on high cardiometabolic risk. A total of 876 subjects (62% females) of Central European descent, aged 20–81 years, were included. International Diabetes Federation (IDF) criteria were employed to classify MS. Continuous scores were calculated. We used the receiver operating characteristics (ROC) analysis to estimate the cutoff value to determine the proportion of MS-free subjects on increased risk. Using the waist circumference, 38% of males and 23% of females presented MS. ROC area under the curves (90%–98%) showed an acceptable performance of both scores to classify the presence of MS. Up to 18% of MS-free males and up to 10% of females displayed continuous score ≥ the relevant cutoff point. The waist-to-height ratio performed similar results. Both continuous scores were proven credible for assessing cardiometabolic risk in MS-free subjects. Clinically, this is important for earlier intervention. Despite minor differences between waist circumference and waist-to-height ratio, it would be appropriate to objectify it using reference population. Novelty: The first study using Z-MSS/siMSS (population-specific Z-score/continuous score of MS) to estimate cardiometabolic risk in Slovak adults. A proportion of MS-free Slovak adults is on high cardiometabolic risk. Difference between using waist circumference and the waist-to-height ratio does not seem to be major in our cohort.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".