Implantable Loop Recorder Allows an Etiologic Diagnosis in One-Third of Patients
Bibliographic record
Abstract
BACKGROUND: The implantable loop recorder (ILR) is a useful tool for diagnosing paroxysmal conditions potentially related to arrhythmias. Most investigations have focused on selected clinical studies or high-volume centers. The aim of this study was to evaluate the indications and outcomes of the ILR in real clinical practice. METHODS AND RESULTS: This was a prospective, multicenter registry of patients undergoing ILR implantation for clinical indications (April 2006-December 2008). Clinical characteristics (symptoms, arrhythmias, treatments) were recorded in a database. Follow-up data at 1 year or after the occurrence of the first episode were also recorded. Total enrollment: 743 patients (male, 413, 55.6%; 64.9 ± 16 years); 228 (30.7%) had structural heart disease (SHD), and 183 (24.6%), bundle branch block (BBB). Recurrent syncope (76.4%) was the most common indication for implantation. Complete follow-up was obtained for 680 patients (91.5%). Three hundred and twenty-five patients (48%) presented 414 events, with a final diagnosis in 230 patients (70.8% of patients with events; 33.1% of patients with follow-up). Syncope secondary to bradyarrhythmia was the most frequent diagnosis. Similar rates of final diagnoses were noted in subgroups of SHD, BBB and normal heart. Regarding the cause of implantation, higher event rates were registered among patients with recurrent syncope. CONCLUSIONS: One-third of patients obtained a final diagnosis with the ILR, independent of the baseline characteristics. Only the cause of implantation provided different rates of final diagnosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 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".