Correction to: Rare variants in KDR, encoding VEGF Receptor 2, are associated with tetralogy of Fallot
Bibliographic record
Abstract
The original article can be found online at https://doi.org/10.1038/s41436-021-01212-y. Correction to: Genetics in Medicine 2021; https://doi.org/10.1038/s41436-021-01212-y; published online 10 June 2021 Due to a processing error the author's Doris Škorić-Milosavljević, Najim Lahrouchi, Alex V. Postma, Connie R. Bezzina were assigned to affiliation 38. However, affiliation 38 does not exist. In addition, the affiliations of Najim Lahrouchi, Elisabeth M. Lodder, and Connie R. Bezzina should be number 1 instead of number 2. The correct affiliation is Department of Clinical and Experimental Cardiology, Amsterdam University Medical Center, Amsterdam, The Netherlands. The original article has been corrected. These authors contributed equally: Doris Škorić-Milosavljević, Najim Lahrouchi, Fernanda M. Bosada, Alex V. Postma, Connie R. Bezzina. A list of authors and their affiliations appears online. Rare variants in KDR, encoding VEGF Receptor 2, are associated with tetralogy of FallotGenetics in MedicineVol. 23Issue 10PreviewRare genetic variants in KDR, encoding the vascular endothelial growth factor receptor 2 (VEGFR2), have been reported in patients with tetralogy of Fallot (TOF). However, their role in disease causality and pathogenesis remains unclear. Full-Text PDF Open Access
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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 | 0.002 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.069 | 0.024 |
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".