A novel algorithm for classification of interatrial communications within the oval fossa in the newborn
Bibliographic record
Abstract
Abstract Background: An interatrial communication is present in most neonates. The majority are considered the “normal” patency of the oval foramen, while a minority are abnormal atrial septal defects. Differentiation between the two with transthoracic echocardiography may be challenging, and no generally accepted method of classification is presently available. We aimed to develop and determine the reliability of a new classification of interatrial communications in newborns. Methods and Results: An algorithm was developed based on echocardiographic criteria from 495 newborns (median age 11[8;13] days, 51.5% females). The algorithm defines three main categories: patency of the oval foramen, atrial septal defect, and no interatrial communication as well as several subtypes. We found an interatrial communication in 414 (83.6%) newborns. Of these, 386 (93.2%) were categorised as patency of the oval foramen and 28 (6.8%) as atrial septal defects. Echocardiograms from another 50 newborns (median age 11[8;13] days, 36.0% female), reviewed by eight experts in paediatric echocardiography, were used to assess the inter- and intraobserver variation of classification of interatrial communications into patency of the oval foramen and atrial septal defect, with and without the use of the algorithm. Review with the algorithm gave a substantial interobserver agreement (kappa = 0.66), and an almost perfect intraobserver agreement (kappa = 0.82). Without the use of the algorithm, the interobserver agreement between experienced paediatric cardiologists was low (kappa = 0.20). Conclusion: A new algorithm for echocardiographic classification of interatrial communications in newborns produced almost perfect intraobserver and substantial interobserver agreement. The algorithm may prove useful in both research and clinical practice.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".