Assessment of the Efficacy of An Osteopathic Treatment in Infants with Biomechanical Impairments to Suckling
Bibliographic record
Abstract
Breastfeeding can be challenging for mother-infant dyads experiencing biomechanical suckling difficulties. Although lactation consultants (LCs) all over the world have increased their skills in this field and can provide support to help position the infant at the breast, the impact of their intervention might be limited in the presence of stiff structures in the infants. Here we present a protocol for a randomized controlled trial to assess the efficacy of osteopathic treatment, coupled with lactation consultation, for infants' biomechanical suckling difficulties. It proposes a set-up and a sequence of actions to ensure an optimal context for treatment, as well as a blinding of parents and LCs to the intervention. Data such as the infant's latch ability measured with the LATCH Assessment Tool, the mother's nipple pain with a visual analog scale (VAS), and the mother's perceptions are collected by LCs four times over a 10-day period. Osteopathic lesions are documented by the osteopath, using a standardized assessment grid. Structures of interest are coherent with the anatomical zones involved in latching onto the breast. This protocol also proposes a strategy to document systematically an osteopathic profile of infants with biomechanical suckling difficulties in their first weeks of life. The implementation of this protocol confirms its feasibility for osteopathic assessment and treatment and paves the way for future trials to further explore the efficacy of osteopathic techniques for infants with biomechanical suckling difficulties.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".