Why do we need supercomputers to understand the electrocardiographic T wave?
Bibliographic record
Abstract
OBJECTIVE: Propagation of depolarisation and repolarisation in myocardium results from an interplay of membrane potential, transmembrane current, and intercellular current. This process can be represented mathematically with a reaction-diffusion (RD) equation. Solving RD equations for a whole heart requires a supercomputer. Therefore, earlier models used predefined action potential (AP) shapes and fixed propagation velocities. We discuss why RD models are important when T waves are studied. METHODS: We simulated propagating AP with an RD model of the human heart, which included heterogeneity of membrane properties. Computed activation times served as input to a model that used predefined AP, and to a "hybrid model" that computed AP only during repolarisation. The hybrid model was tested with different spatial resolutions. Electrocardiograms (ECGs) were computed with all three models. RESULTS: Computed QRS complexes were practically identical in all models. T waves in the fixed-AP model had 20 to 40% larger amplitudes in leads V1-V3. The hybrid model produced the same T waves as the RD model at 0.25-mm resolution, but underestimated T-wave amplitude at lower resolutions. CONCLUSION: Fixed AP waveforms in a forward ECG model lead to exaggerated T waves. Hybrid models require the same high spatial resolution as RD models.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.011 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".