A Qualitative Exploration of Current Approaches to Evaluation and Treatment Decision-Making for Velopharyngeal Insufficiency Following Cleft Palate Repair
Bibliographic record
Abstract
ObjectiveTo explore current approaches to evaluation and treatment decision-making for velopharyngeal insufficiency (VPI) following cleft palate repair.DesignCross-sectional, qualitative study.Setting/participantsParticipants included 28 surgeons and 17 speech-language pathologists (SLPs) from 12 cleft teams in the United States and Canada.InterventionsSemi-structured, qualitative interviews were conducted exploring participants' current approaches to VPI evaluation and treatment decision-making. Data were analyzed using thematic analysis.ResultsParticipants viewed VPI evaluation as a joint effort of surgeons and SLPs. SLPs were entrusted to conduct formal speech evaluations. All teams completed a perceptual evaluation of speech resonance that included spontaneous speech and phrase repetition. Teams varied in their use of patient questionnaires and nasometry. Most teams performed nasoendoscopy as part of their evaluation, although providers noted limited cooperation in younger children and those with developmental delay. Treatment decision-making occurred either as a joint effort between the evaluating surgeon and SLP or exclusively by the surgeon. Most surgeons employed a personalized approach to treatment decision-making, with 19 distinct approaches described. Elements frequently considered in surgical procedure selection included velopharyngeal gap size and closure pattern as seen on nasoendoscopy. Providers were satisfied with their current approach, although most identified opportunities for improvement.ConclusionsCleft teams have individualized approaches to VPI evaluation and treatment decision-making, with the only common element being completion of a perceptual speech evaluation by an SLP. These findings suggest that efforts to implement new approaches will need to be customized to each team's current process.
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 | 0.045 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.015 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".