High Heterogeneity in Prenatal Detection of Severe Congenital Heart Defects Among Physicians, Hospitals and Regions in Quebec
Bibliographic record
Abstract
BACKGROUND: Prenatal detection rates (PDRs) of severe congenital heart defects (SCHDs) are often presented as regional and national aggregates, which might hide significant heterogeneity in PDRs among physicians, hospitals, and regions. The objective was to quantify the variability in the sensitivity of second-trimester ultrasound examination (U/S) to detect SCHDs, and to identify at which level this variability was the greatest. METHODS: This was a retrospective observational cohort of all pregnancy-child dyads with SCHDs in Quebec between 2007 and 2015. We matched the clinical data from the hospitals with the administrative data from the health care system. The variability at each level was estimated using multilevel models by calculating intraclass correlation coefficients. RESULTS: Of 1274 SCHD, 697 were diagnosed prenatally following a referral for a suspected cardiac anomaly on U/S, yielding a sensitivity of 54.7% (95% confidence interval [CI], 52.0%-57.4%). Significant heterogeneity was observed among physicians, hospitals, and regions with the greatest heterogeneity among physicians. The U/S sensitivities in the lowest quartile for physicians, hospitals, and regions were 27.4%, 29.0%, and 39.8%, and those in the highest quartile were 87.3%, 70.1%, and 62.9%, respectively. The mean difference of sensitivity between the lowest and highest quartiles was 59.9% (95% CI, 51.7-68.1) for physicians, and 41.1% (95% CI, 30.3-51.9) for hospitals. The intraclass correlation coefficients at the physician level indicated the greatest heterogeneity among physicians (intrahospital). CONCLUSIONS: There was considerable heterogeneity in PDRs between physicians and hospitals. The driver of the heterogeneity seemed to be at the physician level, with higher interphysician variability. Any measures of improvement should be directed to the physician level.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".