Sviluppo di eventi cardiovascolari in un’ampia popolazione di pazienti affetti da artrite reumatoide con o senza diabete
Bibliographic record
Abstract
INTRODUCTION: Rheumatoid arthritis (Ra) and diabetes are often associated with chronic multimorbidity and share the high risk of development of major cardiovascular events (Mace). This study aimed to identify and analyse patients with only Ra, Ra + diabetes, and only diabetes, in terms of comorbidities and new occurrence of Cv events, from the perspective of the Italian national health service (Inhs). METHODS: Starting from the Fondazione ricerca e salute (ReS)'s database, through the record linkage of administrative healthcare data, cohorts with only Ra, Ra + diabetes and only diabetes have been selected, characterized (age and sex), and analysed by comorbidity (depression, dyslipidemia, hypertension, hemorrhagic stroke and ischemic stroke/transient ischemic attack - Tia, coronary artery disease - Cad, heart failure - Hf, chronic liver disease, periphery artery disease - Pad, chronic kidney disease, asthma/chronic obstructive pulmonary disease - Copd, neoplasia) and by new Cv events (Hf, Cad and ischemic stroke/Tia) within two follow-up years (Kaplan-Meier curves). A logistic regression model defined contribution and type of association of some variables on new Cv events. RESULTS: In 2018, from 5.375.531 Inhs beneficiaries in the ReS database, 13.698 (0.25%) were affected by only Ra, 1728 (0.03%) by Ra + diabetes, 347,659 (6.8%) by only diabetes. The only Ra cohort was composed by more females, younger and with less comorbidities patients. Proportions of 79.3%, 70.8% and 38.5% of patients with Ra + diabetes, only diabetes and only Ra were affected by 2 to ≥4 comorbidities: among patients with Ra + diabetes, comorbidities showed the highest frequencies, mainly hypertension, dyslipidemia and asthma/Copd. Within two follow-up years, about 8% of patients with diabetes with/without Ra developed a new Cv event (vs 3% with only Ra). The presence of Ra/diabetes or Ra + diabetes, male sex, older age and comorbidities of interest resulted significantly (p<0.01) associated with a higher Cv risk. CONCLUSIONS: Comorbidities and the co-presence of diabetes in patients with Ra determine a complicated framework with high risk of Cv events. It is worthy include more complex patients in clinical trials, in order to generate evidence useful for even more multidisciplinary medical teams.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.004 | 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".