Loop recorders for syncope evaluation: what is the evidence?
Bibliographic record
Abstract
INTRODUCTION: Implantable loop recorders (ILRs) have become an important pillar in the diagnostic work-up of patients with unexplained syncope. Areas covered: The modern ILR is minimally-invasive and provides an extended-duration of monitoring with high diagnostic yield. These insertable cardiac monitors (ICMs) enable prolonged monitoring that extends the opportunity for symptom-rhythm correlation from days-weeks to years, with associated incremental diagnostic yield. Over the years, observational studies and randomized trials have supported a unique role for ILRs in the assessment of recurrent or high-risk unexplained syncope. Furthermore, ILRs have been used in the elderly, in children, patients with overt heart disease, and patients with conduction block with varying success. The current guidelines recommend ILRs in the early phase of evaluation in recurrent syncope, and as part of the comprehensive evaluation of high-risk patients with syncope. Expert commentary: In this review, we discuss the evidence surrounding ILRs, including comparison studies with 'conventional' management and the external loop recorder.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".