Quality of life of children and young adults with and without cardiovascular implantable electronic devices during the COVID‐19 pandemic
Bibliographic record
Abstract
BACKGROUND: Children with cardiovascular implantable electronic devices (CIEDs) have a lower quality of life (QoL) compared to the general pediatric population. The combined effect of COVID-19 and CIEDs on the QoL and physical activity (PA) both within and between each population is unknown. METHODS: Individuals 8-25-year-old with and without CIED's were recruited to complete a phone survey. Data collection included demographics, the Pediatric Quality of Life Inventory (PedsQL), and the (PAQ). PedsQL and PAQ scores range from 0-100 and 1-5, respectively, with higher scores indicating a higher QoL and PA. RESULTS: Of the 190 individuals contacted, 148 participated (CIED n = 76, non-CIED n = 72), for an 81% response rate. Participants with and without CIEDs were similar in age (15.5 vs. 16 years, p = .57), gender (male = 57% vs. 42%, p = .07), and self-identified race (white = 79% vs. 81%, p = .44). CIED participants had a lower QoL (70.8 vs. 83.3, p < .001). Lower total scores were noted in CIED participants with structural heart disease compared to those without (71.6 vs. 83.6, p = .035) and those with a history of non-CIED heart or chest surgery compared to those without (71.3 vs. 83.3, p = .035). PAQ scores were similarly lower for CIED participants (2.17 vs. 2.73, p < .001). CONCLUSION: The presence of a CIED negatively impacts the QoL and PA of the pediatric population in the setting of the COVID-19 pandemic. Further research is needed to better understand and address the drivers of decreased QoL and PA in the pediatric CIED population in the setting of the COVID-19 Pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".