Speech outcomes in patients rehabilitated with maxillary obturator prostheses after maxillectomy: a prospective study.
Bibliographic record
Abstract
PURPOSE: Speech outcome measurements are valuable in guiding treatment and determining the effectiveness of rehabilitation with a maxillary obturator prosthesis in individuals with palatal resection. Although speech outcome data exist in the literature for such patients, relatively few reports have used clinical tools designed to measure the acoustic, physiologic, and perceptual bases of speech. This investigation reports these measures for individuals rehabilitated with a maxillary obturator. MATERIALS AND METHODS: Speech measurements were collected prospectively at three clinical visit times (preoperative, postresection without an obturator, and with a definitive obturator) for 12 patients assigned to three groups based on the extent of their resection (< half the hard palate, > or = half the hard palate, hard and soft palates). Acoustic data were obtained with the Nasometer, aeromechanical data were collected with the PERCI-SARS, and perceptual ratings of speech intelligibility were obtained through listener analysis. RESULTS: Significant differences existed among the three treatments for all dependent variables and revealed that speech without an obturator is significantly different from the preoperative state, while speech with an obturator does not differ significantly from preoperative function. Individuals with soft palate involvement exhibited significantly poorer nasalance values than individuals with involvement of the hard palate only. CONCLUSION: Rehabilitation with a maxillary obturator is successful in restoring preoperative speech function. Rehabilitation of individuals with involvement of the soft palate may be more challenging.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".