A novel, simple scale for assessing the symptom severity of atrial fibrillation at the bedside: The CCS-SAF Scale
Bibliographic record
Abstract
BACKGROUND: The severity of symptoms caused by atrial fibrillation (AF) is extremely variable. Quantifying the effect of AF on patient well-being is important but there is no simple, commonly accepted measure of the effect of AF on quality of life (QoL). Current QoL measures are cumbersome and impractical for clinical use OBJECTIVE: To create a simple, concise and readily usable AF severity score to facilitate treatment decisions and physician communication. METHODS: The Canadian Cardiovascular Society (CCS) Severity of Atrial Fibrillation (SAF) Scale is analogous to the CCS Angina Functional Class. The CCS-SAF score is determined using three steps: documentation of possible AF-related symptoms (palpitations, dyspnea, dizziness/syncope, chest pain, weakness/fatigue); determination of symptom-rhythm correlation; and assessment of the effect of these symptoms on patient daily function and QoL. CCS-SAF scores range from 0 (asymptomatic) to 4 (severe impact of symptoms on QoL and activities of daily living). Patients are also categorized by type of AF (paroxysmal versus persistent/permanent). The CCS-SAF Scale will be validated using accepted measures of patient-perceived severity of symptoms and impairment of QoL and will require ‘field testing’ to ensure its applicability and reproducibility in the clinical setting CONCLUSIONS: This type of symptom severity scale, like the New York Heart Association Functional Class for heart failure symptoms and the CCS Functional Class for angina symptoms, trades precision and comprehensiveness for simplicity and ease of use at the bedside. A common language to quantify AF severity may help to improve patient care.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".