Practice Spotlight: Pharmacists in a Multidisciplinary Atrial Fibrillation Clinic
Bibliographic record
Abstract
Atrial Fibrillation Clinic opened in October 2009 with the mandate of streamlining care for patients with atrial fibrillation who have had suboptimal response to standard therapies and therefore require more complex medication management and/or radiofrequency ablation.The proven benefits of this treatment modality have resulted in broader use and hence long waiting lists for the procedure.It was anticipated that a multidisciplinary approach to care would increase patients' access to clinicians familiar with the various treatment options.Therefore, the clinic employs a registered nurse, a nurse practitioner, and a clinical pharmacist (with Sonia Basi and Christine Yu sharing the clinical pharmacist role), all of whom work closely with a team of electrophysiologists.Each discipline utilizes the full extent of its professional authorities for triaging, selection of treatment, and follow-up.][3] However, for the St Paul's Hospital Atrial Fibrillation Clinic, the specific roles and activities for each discipline were developed in house, since this was the first such clinic in the province of British Columbia.Cardiologists make most referrals to the clinic, but patients may also be referred by general internists, family physicians, other electrophysiologists, or emergency physicians.The clinic nurse conducts a brief triage interview by telephone with each patient to assess the severity and urgency of symptoms.All patients with documented atrial fibrillation or flutter are entered into clinic care and scheduled for an hour-long group
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.067 | 0.009 |
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".