Does Familiarity with Technology Predict Successful Use of an External Loop Recorder? The Loop Recorder Technology Cognition Study (LOCO)
Bibliographic record
Abstract
BACKGROUND: The diagnosis of presyncope, syncope, and palpitations is facilitated by successful documentation of the cardiac rhythm during symptoms. We prospectively assessed technological familiarity using a Technology Cognition Questionnaire to determine influence on proper and effective use of an external loop recorder (ELR). METHODS: Patients with palpitations, presyncope, or syncope were assessed for familiarity with technology and provided an ELR for a period of 6 weeks. Proper use of the device was demonstrated to the patient and test transmissions were sent by analog telephone line on a weekly basis. Patients were instructed to activate the device to record cardiac rhythm when symptoms recurred, and to send these recordings via telephone transmission. RESULTS: Ninety-two patients were prospectively enrolled, with mean age 54.9 +/- 20.9 and 42 males (46%). Sixty-five patients (71%) had recurrence of symptoms during the 6-week monitoring period. Among these patients, 40 (62%) were successful in recording and transmitting data such that a diagnosis was made at a median of 8 days (IQR 12.5, range 0-30). Among patients with symptoms during the monitoring period, 36 (55%) had at least one failed recording or transmission. On multivariate analysis, failed symptom recording/transmission was less likely among patients able to program a home video recorder (odds ratio [OR] 0.25 [0.07-0.93]), and more likely among patients who failed a test transmission (OR 3.45 [1.04-11.7]). No variables were independently associated with successful diagnosis. CONCLUSIONS: Familiarity with technology correlates with successful use of the ELR, but does not necessarily correlate with the ability to reach a 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".