Pitfalls of pacemaker detection of ventricular high-rate events
Bibliographic record
Abstract
Key Teaching Points•When a pacemaker fails to record a ventricular high-rate (VHR) event, true vs functional undersensing should be considered.•True undersensing refers to failure to detect a VHR event owing to small-amplitude intrinsic signals or the nature of the sensing algorithm.•Functional undersensing occurs if the episode fails to meet the definition of a VHR or is not detected as a result of the way in which refractory or blanking periods are programmed.•Functional undersensing of sustained ventricular tachycardia can occur with Biotronik pacemakers if the ventricular rate exceeds 240 beats per minute owing to the fact that the ventricular refractory period is nominally set to 250 ms and that sensed beats that fall within this refractory period do not increase the VHR counter. •When a pacemaker fails to record a ventricular high-rate (VHR) event, true vs functional undersensing should be considered.•True undersensing refers to failure to detect a VHR event owing to small-amplitude intrinsic signals or the nature of the sensing algorithm.•Functional undersensing occurs if the episode fails to meet the definition of a VHR or is not detected as a result of the way in which refractory or blanking periods are programmed.•Functional undersensing of sustained ventricular tachycardia can occur with Biotronik pacemakers if the ventricular rate exceeds 240 beats per minute owing to the fact that the ventricular refractory period is nominally set to 250 ms and that sensed beats that fall within this refractory period do not increase the VHR counter.
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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 | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".