Center-Related Determinants of VKA Anticoagulation Quality: A Prospective, Multicenter Evaluation
Bibliographic record
Abstract
BACKGROUND: Center-specific TTR (c-TTR) is a measure reporting the mean patient TTR within an anticoagulation clinic describing the quality of anticoagulant monitoring offered by that clinic. c-TTR has a considerable between-center variation, but its determinants are poorly understood. OBJECTIVES: We aimed at evaluating which clinical, procedural or laboratory factors could be associated with c-TTR variability in a multicenter, observational cross-sectional study over a five-year period. PATIENTS/METHODS: Data from 832,204 individual patients followed for VKA therapy in 292 Centers affiliated with the Italian Federation of Anticoagulation Clinics (FCSA) were analyzed. c-TTR was computed based on the TTR of patients followed at each Center, and a mixed linear regression model was used for a predefined set of explanatory variables. RESULTS: The Center next-visit interval ratio (the mean number of days after a visit with an INR outside the therapeutic range, divided by the days after a visit with an INR within the therapeutic range), the Center mean patient INR and the Center laboratory performance at EQA proficiency testing were the only variables that were independently associated with c-TTR (β-coefficients -17.32, 9.67, and -0.11, respectively; r2 = 0.635). CONCLUSIONS: These findings suggest that c-TTR associates with proactive strategies aimed at keeping patients very close to their target INR with a prompt re-evaluation of those patients with under- or over-therapeutic INR.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".