Selection of a reduced set of parameters for classification of ventricular conduction defects by cluster analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Currently used algorithms for classification of ventricular conduction defects (VCD) are fairly complex and employ a large number of mostly dichotomized ECG features or their combinations, based almost entirely on QRS morphology. The authors used disjoint clustering analysis in an effort to identify a smaller subset of ECG features to characterize various VCD categories using ECG data of 3,501 normal subjects and 887 with VCD. Their results with five parameters forming seven clusters suggest that the criteria for many VCD categories are based on arbitrary decision boundaries, resulting in fact in considerable overlap of normal conduction, fascicular and incomplete blocks even in multidimensional decision space. The same conclusion holds for the so-called undetermined type VCD, which in fact converges nearly completely into the clusters formed by LBBB and RBBB.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it