R‐Wave Sensing in an Implantable Cardiac Monitor without ECG‐Based Preimplant Mapping: Results from a Multicenter Clinical Trial
Bibliographic record
Abstract
INTRODUCTION: Reducing the form factor of an implantable cardiac monitor (ICM) may simplify device implant. This study evaluated R-wave sensing at a range of electrode distances and a preferred device implant location without mapping. METHODS: Patients scheduled for a Medtronic Reveal® ICM implant (Medtronic Inc., Minneapolis, MN, USA) underwent a preimplant pocket recording using a diagnostic recording catheter. The ICM implant location was left to the discretion of the implanting physician, but a "recommended" position spanned the V2 -V3 electrocardiogram electrode location in an oblique 45° angle. R-wave amplitudes were analyzed from ICM follow-up. RESULTS: Seventeen of 41 subjects (15 male, age 57 ± 16 years) had the maximum surface-filtered R-wave at the recommended location. Fourteen patients underwent diagnostic recording across the range of electrode spacing. There was a strong correlation between the R-wave amplitude and electrode distance (r(2) = 0.97, P < 0.001) with an increase of 29 μV per 2.5 mm. Comparing normalized R-wave distributions between the recommended ICM implant group (Group 1, n = 19) and the remaining patients (Group 2, n = 7), the proportion of ICM R-wave counts of amplitude 0.25-1.2 mV was higher (79% vs 46%, P < 0.05). Of 17 patients in Group 1 who had ≥ 1-month ICM follow-up (79 ± 45 days), no sensing-related false arrhythmia detection was found in 16 (93%) patients. CONCLUSIONS: The subcutaneous R-wave amplitude correlates with electrode spacing in the implant zone of ICM patients. Implant locations at the V2 -V3 position at a 45° angle offer an adequate R wave for sensing. Preimplant mapping to achieve acceptable R-wave amplitude may not be necessary.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".