The Effectiveness of Transtelephonic Monitoring of Pacemaker Function in Pediatric Patients
Bibliographic record
Abstract
BACKGROUND: To determine the sensitivity and specificity, rate of compliance, and predictors of failure of telephone transmission of pacemaker function in a pediatric population. METHODS: A total of 2,638 pacemaker transmission records were reviewed retrospectively. Standard calculations of sensitivity, specificity, and positive and negative predictive values were performed. Longitudinal data analysis was used to detect factors influencing the effectiveness of transtelephonic monitoring. The proportion of missed transmissions was calculated, thus enabling assessment of compliance. Logistic regression was performed to determine predictors of poor compliance. RESULTS: Telephone transmission of pacemaker function, as a diagnostic tool, had a sensitivity of 94.8%, specificity of 99.2%, positive predictive value of 82.1%, and negative predictive value of 99.9%. Longitudinal analysis failed to show any significant predictors of transmission failure. Compliance with a prescribed transmission reached 84.5% in our patient population. Logistic regression analysis failed to identify any predictors of noncompliance. CONCLUSION: Values for sensitivity and specificity indicate that telephone transmission is a useful diagnostic tool for assessing pacemaker function at a distance. Negative predictive value is 99.9%, indicating that normal telephone transmissions are very reassuring of normal pacemaker function. Telephone transmission is equally successful in all age groups, genders, distances from a tertiary referral center, underlying diagnoses, pacing modes, and pacemaker models. Compliance with telephone transmission follow-up was higher in our population than in previous studies.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".