Authors’ reply re: Maternal transmission of SARS-COV-2 to the neonate, and possible routes for such transmission: A systematic review and critical analysis. (Response to BJOG-20-1416)
Bibliographic record
Abstract
Authors’ reply re: ’Maternal transmission of SARS-COV-2 to the neonate, and possible routes for such transmission: A systematic review and critical analysis (Response to BJOG-20-1416)Kate F Walker1, Keelin O’Donoghue2, Nicky Grace3, Jon Dorling4, Jeannette L Comeau4, Wentao Li5 Jim G Thornton11Division of Child Health, Obstetrics and Gynaecology, School of Medicine, University of Nottingham2The Irish Centre for Maternal and Child Health, University College Cork, Cork University Maternity Hospital, Cork, Ireland3 School of English, University of Nottingham4Department of Pediatrics, Faculty of Medicine, Dalhousie University, Halifax, Nova Scotia, Canada5Department of Obstetrics and Gynaecology, Monash University, Clayton, AustraliaThank you for the opportunity to comment on the letter by Dr Xue from Shanghai Jiao Tong University. We agree there are many weaknesses in the data we reviewed. Dr Xue has identified one. Others are the incomplete reporting of infant feeding and mother-child interactions, and the frequent lack of infant testing to confirm or refute the possibility of vertical transmission of COVID-19. Finally, although we simply provided summary totals, it would be statistically preferable to combine series using the Mantel-Haenszel method and calculate a relative risk. We judged that doing this in light of the uncertainties around the data which Dr Xue has identified, might give a spurious precision to our results. As he says, more work is needed. For now we think it remains reasonable to not regard COVID-19 in itself, as an indication for Caesarean, artificial feeding or separation, in the mother and baby’s interest.
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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 | 0.018 | 0.166 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.028 | 0.023 |
| Insufficient payload (model declined to judge) | 0.027 | 0.008 |
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".