Multigroup diagnostic classification using body surface potential maps
Bibliographic record
Abstract
Multivariate analysis was performed on 120-lead electrocardiographic (ECG) data in order to derive diagnostic criteria for correct classification of 159 normal subjects (N), 103 patients with anterior myocardial infarction (AMI), 130 patients with inferior myocardial infarction (IMI), and 116 patients with pure left ventricular hypertrophy (LVH). The analysis used instantaneous voltage measurements obtained by sampling the time-normalized P, PR, QRS, and STT waveforms at equal intervals. The durations of these waveforms were measured prior to time normalization. Linear discriminant functions were computed for each possible bigroup comparison, and the six best discriminators of each pairwise comparison were selected for the final multigroup classification model. A total of eight features from five torso sites accounted for the correct assignment of 93% of N, 92% of AMI, 94% of IMI, and 82% of LVH. The misclassification matrix illustrated the relatively high rate of false negatives in LVH (11%). The improvement in classification over the standard 12 lead ECG was highest for LVH (11%) and lowest for N (4%); AMI and IMI rates were improved by 7% and 6%, respectively.>
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".