Bibliographic record
Abstract
OBJECTIVE: The instant of maximum slope (Tup) of the T wave in the unipolar electrogram is a well-established measure of repolarisation time (TR). Nevertheless, recent observations on positive T waves have caused a renewed debate. The purpose of this study was to elucidate the mechanism that leads to positive and negative T waves and to investigate which electrogram feature best predicts TR. METHODS: We simulated propagating action potentials (AP) and electrograms with a bidomain reaction-diffusion model of the human heart including heterogeneous ion-channel properties. To explain positive T waves we compared results with those of a much simpler model, which predicts T waves from local and remote AP. RESULTS: Repolarisation time was defined as the instant of steepest downstroke of the AP. T wave polarity was mostly determined by TR. Positive T waves occurred at early-repolarising sites. Correlation between Tup and TR was >0.99, in both negative and positive T waves. T wave area and peak value also correlated highly with TR. CONCLUSION: The polarity of the T wave is primarily determined by TR. Positive T waves occur at early-repolarising sites. Local TR is best estimated by Tup, also in positive T waves.
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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 | 0.000 | 0.002 |
| 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) | 0.008 | 0.002 |
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".