A Digital Health System to Assist Family Physicians to Safely Prescribe NOAC Medications
Bibliographic record
Abstract
Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Generally, the therapeutic options for managing AF include the use of anticoagulant drugs that prevent the coagulation of blood. New Oral Anticoagulants (NOACs) are not optimally prescribed to patients, despite their efficacy. In Canada, NOAC medications are not directly available to patients who belong to provincial benefits programs, rather a NOAC special authorization process establishes the eligibility of a patient to receive a NOAC and be paid by the provincial Pharmacare program. This special authorization process is tedious and paper-based which inhibits physicians to prescribe NOAC leading to suboptimal AF care to patients. In this paper, we present a computerized NOAC Authorization Decision Support System (NOAC-ADSS), accessible to physicians to help them (a) determine a patient eligibility for NOAC based on Canadian AF clinical guidelines, and (b) complete the special authorization form. We present a semantic web based system to ontologically model the NOAC eligibility criteria and execute the knowledge to determine a patient NOAC eligibility and dosage.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.076 | 0.028 |
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".