Comparison of speech and resonance outcomes across three methods of treatment for maxillary defects
Bibliographic record
Abstract
Purpose: Treatment of maxillary defects, whether by prosthetic rehabilitation or surgical reconstruction, should aim to restore speech function and resonance balance. With the advent of technology and changing clinical practices related to maxillary defect management, speech outcomes need to be evaluated and compared in order to determine efficacy of differing approaches. Materials and Methods: One hundred and four patients across three treatment groups for maxillary defects were included: 38 patients who were treated with maxillary obturators (OBT group), 39 patients who were treated with a standard fibular free flap reconstruction that did not involve digital planning of the reconstruction (Standard group) and 27 patients who were reconstructed using a digitally planned surgical design and simulation fibular free flap reconstruction (SDS group). Speech assessments were completed to assess word and sentence intelligibility, resonance balance and aeromechanical orifice estimation among these three groups. Assessments included the Computerized Assessment of Intelligibility of Dysarthric Speech (C-AIDS), nasalance scores via the Nasometer and palatopharyngeal orifice area via the PERCI-SARS. Results: Significant differences were found in word intelligibility between the SDS and the Standard groups (p =.035) and on nasalance scores between the SDS and the OBT groups (p=.027). Conclusions: Patients treated with digital reconstruction (SDS) had better speech outcomes than the other two treatment groups, whose mean scores on certain speech variables were not within normal limits. Speech outcomes in the SDS group were consistently within the normal range across all measured speech variables.(Int J Maxillofac Prosthetics 2017;1:2-8)
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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 | 0.000 |
| 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) | 0.002 | 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".