Assessment of new onset postcoronary artery bypass surgery atrial fibrillation: current practice pattern review and the development of treatment guidelines
Bibliographic record
Abstract
BACKGROUND: The incidence of postcoronary artery bypass graft (CABG) atrial fibrillation (AF) is 22.5% at the QEII Health Sciences Centre and the mean length of stay is 5.5 days greater than for those patients who do not experience this complication. Appropriate pharmacological management is important to prevent the potential morbidity from AF, such as thromboembolism, congestive heart failure, cardiogenic shock and coronary ischemia. This project compared current practice patterns in the management of post-CABG AF with local practice pattern beliefs and evidence from the primary literature. Subsequently, treatment guidelines were designed to help guide a rationale treatment approach. OBJECTIVE: To promote appropriate treatment strategies for post-CABG AF and increase local practitioner awareness of drug-use outcomes by developing consensus treatment guidelines. DESIGN: There were three phases. In phase 1, a retrospective chart analysis of 35 post-CABG AF patients over three consecutive months was conducted to assess current practice patterns. All published studies on this subject were also collected and analysed. A survey of pharmacological treatment preferences was distributed to local stakeholders during phase 2. The third phase involved the development and implementation of treatment guidelines. RESULTS: This study identified a highly variable approach to the treatment of post-CABG AF. The mean number of agents used to treat AF post-CABG was two (range 1-4); all patients (100%) were prescribed rate-controlling agents and 37% were prescribed an antiarrhythmic drug. There was also a mismatch between practice pattern beliefs and actual practice. CONCLUSIONS: This reinforced the need for a consistent treatment approach that was facilitated with the development and implementation of local guidelines.
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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 | 0.031 | 0.100 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 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".