Infant sucking ability, non-organic failure to thrive, maternal characteristics, and feeding practices: a prospective cohort study.
Bibliographic record
Abstract
This prospective study examined the relation of neonatal sucking to later feeding, postnatal growth, maternal postpartum depression, and feeding practices. Healthy infants of at least 37 weeks gestational age were recruited. At 1 week of age, a strain-gage device was attached to the infant's cheeks during sucking to identify sucking efficiency. Two-hundred and two infants (100 males, 102 females; mean age 39.6 weeks, SD 1.1 weeks) with efficient sucking and 207 (101 males, 106 females; mean gestational age 39.4 weeks, SD 1.2 weeks) with inefficient sucking were identified. Growth was measured at 2, 6, 10, and 14 months. Mothers completed a feeding questionnaire and the Edinburgh Postnatal Depression Scale at the same testing points. While 18 infants (5%) showed a downward shift in growth, their clinical picture did not present as non-organic failure to thrive (NFTT). Inefficient neonatal sucking did not predict postnatal growth, later feeding difficulties, nor maternal feeding practices, but concurrent inefficient feeding did. Maternal depression did not affect feeding practices, infant feeding abilities, nor growth, suggesting that the importance of maternal postpartum depression in association with feeding may be less than previously assumed. The term NFTT, therefore, merits reexamination and a more focused definition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".