Predicting the Outcome of Patients with Unexplained Syncope Undergoing Prolonged Monitoring
Bibliographic record
Abstract
Patients with unexplained syncope are often considered candidates for prolonged monitoring or empiric pacing when noninvasive and invasive investigations fail to provide a diagnosis. Identifying the outcome of patients undergoing prolonged monitoring that would ultimately benefit from empiric pacing may permit a cost-effective approach to resolution of syncope. Two hundred and six patients (age 57 +/- 18 years, 57% male) underwent prolonged monitoring with an implanted loop recorder for syncope of unknown origin. The median number of previous syncopal episodes was four (mean 29 +/- 133). Prior tilt testing was performed in 63% of patients, and electrophysiological testing in 46%. Symptoms recurred during follow-up in 142 patients (69%). Recurrence was associated with bradycardia leading to pacemaker implantation in 35 patients (17.0%), tachycardia in 12 (5.8%), sinus rhythm in 63 (30.6%), neurally mediated syncope based on rhythm and clinical assessment in 22 (11%), and failed activation in 10 (5%). Logistic regression analysis of baseline variables found that age was the only independent variable that predicted the need for pacing, associated with a 3% increase in risk per advancing year of age (odds ratio 1.027, P = 0.026). Despite this finding, no age group could be identified in which the likelihood of requiring pacing exceeded 30%. Logistic regression also found that patients with structural heart disease were less likely to experience recurrent symptoms during monitoring (49% vs 78%, P = 0.001) and that advancing age was associated with earlier recurrence of symptoms (P = 0.01). The etiology of recurrent syncope is diverse and cannot be predicted by baseline clinical variables. Empiric pacing appears to have little role in the management of this patient population.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".