Implementation of Virtual Fontan Heart Camps During a Pandemic
Bibliographic record
Abstract
BACKGROUND: Children with a Fontan operation represent a unique form of congenital heart disease (CHD) that requires multiple cardiac surgeries and procedures with an uncertain long-term outcome. Given the rarity of the types of CHD that require this procedure, many children with a Fontan do not know any others like them. METHODS: With the cancelation of medically supervised heart camps due to the COVID-19 pandemic, we have organized several physician-led virtual day camps for children with a Fontan operation to connect with others in their province and across Canada. The aim of this study was to describe the implementation and evaluation of these camps via the use of an anonymous online survey immediately after the event and reminders on days 2 and 4 postevent. RESULTS: Fifty-one children have participated in at least 1 of our camps. Registration data showed that 70% of participants did not know anyone else with a Fontan. Postcamp evaluations showed that 86% to 94% learned something new about their heart and 95% to 100% felt more connected to other children like them. CONCLUSION: We have demonstrated the implementation of a virtual heart camp to expand the support network for children with a Fontan. These experiences may help to promote healthy psychosocial adjustments through inclusion and relatedness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".