Oral anticoagulation across diabetic subtypes in patients with newly diagnosed atrial fibrillation: A report from the <scp>GARFIELD‐AF</scp> registry
Bibliographic record
Abstract
AIMS: This study aims to describe both management and prognosis of patients with diabetes mellitus (DM) and newly diagnosed atrial fibrillation (AF), overall as well as by antidiabetic treatment, and to assess the influence of oral anticoagulation (OAC) on outcomes by DM status. METHODS: The study population comprised 52 010 newly diagnosed patients with AF, 11 542 DM and 40 468 non-DM, enrolled in the GARFIELD-AF registry. Follow-up was truncated at 2 years after enrolment. Comparative effectiveness of OAC versus no OAC was assessed by DM status using a propensity score overlap weighting scheme and weights were applied to Cox models. RESULTS: Patients with DM [39.3% oral antidiabetic drug (OAD), 13.4% insulin ± OAD, 47.2% on no antidiabetic drug] had higher risk profile, OAC use, and rates of clinical outcomes compared with patients without DM. OAC use was associated in patients without DM and patients with DM with lower risk of all-cause mortality [hazard ratio 0.75 (0.69-0.83), 0.74 (0.64-0.86), respectively] and stroke/systemic embolism (SE) [0.69 (0.58-0.83), 0.70 (0.53-0.93), respectively]. The risk of major bleeding with OAC was similarly increased in patients without DM and those with DM [1.40 (1.14-1.71), 1.37 (0.99-1.89), respectively]. Patients with insulin-requiring DM had a higher risk of all-cause mortality and stroke/SE [1.91 (1.63-2.24)], [1.57 (1.06-2.35), respectively] compared with patients without DM, and experienced significant risk reductions of all-cause mortality and stroke/SE with OAC [0.73 (0.53-0.99); 0.50 (0.26-0.97), respectively]. CONCLUSIONS: In both patients with DM and patients without DM with AF, OAC was associated with lower risk of all-cause mortality and stroke/SE. Patients with insulin-requiring DM derived significant benefit from OAC.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".