Improvement on oral health related quality of life after orthosurgical treatment: a mixed methods study
Bibliographic record
Abstract
The aim of this study was to understand the influence of orthosurgical treatment on oral health-related quality of life (OHRQoL) in patients with Class II and III skeletal malocclusion by conducting a mixed method case series study. Nineteen patients submitted to orthosurgical treatment in a private practice in Brazil were included in the sample. Data were collected retrospectively and interviews were held from March 2020 to July 2021. Patients answered to the Oral Health Impact Profile (OHIP-14) in the first part of the interview and subsequently answered the qualitative questions. The overall mean of OHIP-14 after treatment was 4.21 (SD 4.68). The qualitative data were analyzed according to thematic analysis and four themes emerged from the interviews: a) concept of quality of life, b) pre-treatment life, c) post-treatment life, and d) positive and negative aspects of treatment. Quality of Life was reported by the patients as the absence of feeling pain, having emotional and physical health, having a satisfactory esthetic appearance and self-esteem. Before treatment, most Class II patients used to complain about breathing and sleeping problems, while Class III patients complained more intensely about esthetics. Pain was a common problem reported by both Class II and Class III patients. In general, improvement was perceived in self-esteem, esthetics, function and pain. Complaints about negative aspects of the treatment were restricted to the postoperative period. The orthosurgical treatment was important for improving the OHRQoL of patients in terms of esthetic, functional and psychosocial aspects.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".