Challenges and lessons learned from the Pediatric Heart Network Normal Echocardiogram Database study
Bibliographic record
Abstract
BACKGROUND: The Pediatric Heart Network Normal Echocardiogram Database Study had unanticipated challenges. We sought to describe these challenges and lessons learned to improve the design of future studies. METHODS: Challenges were divided into three categories: enrolment, echocardiographic imaging, and protocol violations. Memoranda, Core Lab reports, and adjudication logs were reviewed. A centre-level questionnaire provided information regarding local processes for data collection. Descriptive statistics were used, and chi-square tests determined differences in imaging quality. RESULTS: For the 19 participating centres, challenges with enrolment included variations in Institutional Review Board definitions of "retrospective" eligibility, overestimation of non-White participants, centre categorisation of Hispanic participants that differed from National Institutes of Health definitions, and exclusion of potential participants due to missing demographic data. Institutional Review Board amendments resolved many of these challenges. There was an unanticipated burden imposed on centres due to high numbers of echocardiograms that were reviewed but failed to meet submission criteria. Additionally, image transfer software malfunctions delayed Core Lab image review and feedback. Between the early and late study periods, the proportion of unacceptable echocardiograms submitted to the Core Lab decreased (14 versus 7%, p < 0.01). Most protocol violations were from eligibility violations and inadvertent protected health information disclosure (overall 2.5%). Adjudication committee reviews led to protocol changes. CONCLUSIONS: Numerous challenges encountered during the Normal Echocardiogram Database Study prolonged study enrolment. The retrospective design and flaws in image transfer software were key impediments to study completion and should be considered when designing future studies collecting echocardiographic images as a primary outcome.
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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 | 0.617 | 0.648 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.011 | 0.012 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".