Canadian Registry of Implantable Electronic Device Outcomes
Bibliographic record
Abstract
BACKGROUND: The Riata lead under advisory has posed a unique clinical scenario where inside-out abrasion results in externalization of conductor cables, with a higher risk of electrical failure. We developed a comprehensive registry to assist with clinical management of this lead. METHODS AND RESULTS: This Canadian registry reports the findings of 3763 (74.2% of all Riata leads in Canada) Riata leads under advisory, with a mean follow-up time of 8.9±1.5 years. The overall electrical failure rate was 5.2% at 8 years, with no difference between 7-French and 8-French lead models. Cable externalization was found to be more common in the 8-French model (12.3% versus 5.2%, P<0.0001) and was associated with a higher risk of electrical failure. Predictors of electrical lead failure included cable externalization, higher left ventricular ejection fraction, younger age, higher body mass index, and a passive fixation lead. One patient died due to electrical failure, a further 2 patients survived an event where the device failed to deliver high-voltage therapy. Major complications because of lead extraction were higher when compared with lead abandonment, no difference among lead model observed. Two deaths occurred as a consequence of lead extraction, in the context of an underlying infection. CONCLUSIONS: The Riata lead under advisory has a steady electrical failure rate over time. There are identifiable predictors of lead failure that can assist with clinical decisions as to whether lead revision should be performed prophylactically.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".