Patient preferences for features associated with leadless versus conventional transvenous cardiac pacemakers
Bibliographic record
Abstract
Abstract Background Regulatory approval of the first dual-chamber leadless pacemaker (PM) system provides patients an alternative to conventional transvenous pacemakers. Objective To quantify patients’ preferences for pacemaker features. Methods Patients with a de-novo PM indication were recruited from 7 US sites to complete a discrete-choice experiment (DCE) survey. Patients chose between pairs of experimentally designed, hypothetical PMs that varied according to PM type (removable leadless, non-removable leadless, conventional transvenous); battery life (5, 8, 12, 15 years); time since regulatory approval (2, 10 years); discomfort for 6 months (none, discomfort); complication risk and infection risk (1%, 5%, 10%/20% for each). Patients with a de-novo pacemaker indication were recruited to complete a web-based survey from seven US sites between May 11, 2022 to May 24, 2023. Results Choice data from 117 patients indicated that complication risks and infection risks were the most influential. On average, patients preferred removable leadless pacemakers over both non-removable leadless pacemakers ( p =0.001) and conventional transvenous pacemakers ( p =0.031). However, latent-class analysis revealed two distinct preference classes. One class preferred leadless pacemakers (50.5%) and the other class preferred conventional transvenous pacemakers (49.5%). The conventional PM class prioritized pacemakers with ten rather than two years since regulatory approval ( p <0.001) whereas the leadless PM class was insensitive to years since regulatory approval ( p =0.83). All else equal, patients would accept maximum risks of complications or infections ranging about 5% to 18% to receive their preferred pacemaker type. Conclusion Latent-class analysis revealed strong patient preferences for the type of PM, with a nearly equal split between recent leadless PM technology and conventional transvenous PMs. These findings can inform shared decision making between healthcare providers and patients.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".