Current Practice Patterns and Training Pathways for Feeding Infants with Cleft Palate
Bibliographic record
Abstract
Objective To examine the current trends and practices across disciplines for feeding infants with cleft palate with or without cleft lip and to describe provider training within this area Design Prospective survey Setting ACPA approved cleft palate teams and healthcare providers in the United States and Canada Participants Interdisciplinary providers that regularly provide feeding services to infants with cleft palate Intervention 50-item survey designed and distributed electronically via the ACPA Main Outcome Measures Information on provider demographics and practice patterns Results 76 respondents included providers in North America that have either currently or previously served on a cleft palate team. The majority of respondents were in speech-language pathology (49%) or nursing (38%) disciplines, worked in an outpatient setting (70%), and received no information (68%) regarding cleft palate feeding in their academic training. While specific practice patterns were relatively consistent across the respondent cohort, provider characteristics were significantly associated with squeezing the Haberman ( p = .013) and likelihood of collaboration with other providers when counseling parents/caregivers ( p = .039). Conclusions While provider characteristics varied, there were similar practice patterns observed across disciplines. Future research is needed explore training related to feeding knowledge as well as practice patterns in locations with a lower patient volume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".