Breastfeeding Following Spinal Cord Injury: Consumer Guide for Mothers
Bibliographic record
Abstract
The World Health Organization (WHO) recommends that infants be breastfed exclusively for the first 6 months of age. However, there are few resources available on the effects a spinal cord injury (SCI) can have for breastfeeding mothers. It is difficult to find information to address the unique challenges women with SCI experience when planning or trying to breastfeed. Our international team, including women with SCI, health care providers, and SCI researchers, aims to address the information gap through the creation of this consumer guide. The purpose of this consumer guide is to share the most common issues women with SCI experience during breastfeeding and provide information, practical suggestions, recommendations, and key resources in lay language. General information about breastfeeding is available on the internet, in books, or from friends and health care providers. We do not intend to repeat nor replace general breastfeeding information or medical advice. Breastfeeding for mothers with SCI is complex and requires a team of health care providers with complementary expertise. Such a team may include family physician, obstetrician, physiatrist, neurologist, occupational and physical therapist, lactation consultant, midwife, and psychologist. We hope this consumer guide can serve as a quick reference guide for mothers with SCI planning of trying to breastfeed. This guide will also be helpful to health care providers as an educational tool.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.018 |
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".