Authors' response
Bibliographic record
Abstract
We thank Dr Jack Newman for his interest in our article “Which method of breastfeeding supplementation is best? The beliefs and practices of paediatricians and nurses” (1). We agree with Dr Newman that breastfeeding is the ideal method of feeding to support growth and development of the infant (2). Its benefits are indispensible to the child and the mother (2). However, the objective of our study was to assess breastfeeding supplementation methods used at hospitals if the mother was temporarily unavailable to breastfeed. There are many cases in which the mother has the intention to breastfeed but, due to unforeseen reasons, cannot breastfeed her child (eg, the mother is discharged before the child, the mother or the child has complications, etc). It was only in these circumstances that we were interested in assessing the breastfeeding supplementation practices used in hospitals and the opinions of the health care staff regarding breastfeeding supplementation. Hence, the methodology of the study was designed to reflect this objective. Therefore, we investigated supplementation methods such as bottle feeding, finger feeding, cup feeding and nasogastric feeding. Lactation aids, on the other hand, are best used when the mother and child, together, face difficulties in breastfeeding. Accordingly, a lactation aid was not assessed as one of the breastfeeding supplementation methods in this study. Lactation aids require the presence of the mother, which defied the objective of our study. Finally, we would like to stress the significance of breastfeeding, and its importance during the first years of life to promote growth, development and family well-being.
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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 | 0.010 | 0.153 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.057 | 0.022 |
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".