An Evaluation of Symptom Classification Systems used for the Assessment of Patients with Heart Failure in France
Bibliographic record
Abstract
Many systems have been proposed to assess the degree of functional impairment in patients with chronic heart failure in order to be able to draw comparisons between patients and assess the development of the disease in the same patient. The NYHA classification is subjective and insufficiently reproducible and has no real predictive value with respect to the exertion test. The Canadian classification does not contribute much in terms of validation. The Feinstein and Duke University classifications are too complex, not very easy to use and have never been validated. The scale of activity proposed by Goldman gives details on functional impairment by using examples from daily activities, selected for their variety and grouped according to the energy that they require. This classification is highly reproducible and is concordant with the exertion test (duration of the exertion test, VO2 max). However, it is not suitable for France. The examples are not precise enough: in addition, they do not eliminate contradictions that can make the patient impossible to classify. We propose a scale of activity specifically designed for use in France. It is reproducible and the VO2 peaks are highly concordant. Lastly, the questions the patient is asked are progressive, thus avoiding contradictory answers. This classification could prove to be useful in everyday life and also for multi-center studies in French-speaking countries.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".