Analysis of Health-Related Quality-of-Life Outcomes and Their Predictive Factors in Pediatric Patients Who Undergo Otoplasty
Bibliographic record
Abstract
BACKGROUND: There are limited data on the effect of otoplasty on health-related quality of life in children with prominent ears. Predictors of health-related quality-of-life outcomes in otoplasty have not been well studied. METHODS: In this retrospective cohort study, 79 patients aged 18 years and younger who underwent otoplasty, and their parents, were asked to complete a survey, which included the Glasgow Children's Benefit Inventory and the Pediatric Quality of Life Inventory, to assess the parent-reported health-related quality-of-life changes and the current health-related quality of life, respectively. Other collected data included demographics, medical history, preoperative psychosocial experiences, motivations and expectations for surgery, postoperative complications, and general satisfaction. RESULTS: Fifty patients (63 percent) replied. Of those, 88 percent reported being more satisfied with the appearance of their ears after otoplasty and 93 percent would choose to have this procedure again if given a second chance. The mean Glasgow Children's Benefit Inventory total score was 24.4 and the mean Pediatric Quality of Life Inventory total score was 94.3 for the Child Self-Report and 93.3 for the Parent Proxy-Report, indicating a positive health-related quality-of-life outcome. Linear regression analysis showed that history of teasing and expectations of a "life-changing" event were significant predictors of the Glasgow Children's Benefit Inventory total score (p<0.01). CONCLUSIONS: In this study, parents reported a significant improvement in their children's health-related quality of life following otoplasty. The children themselves also reported having a generally high health-related quality of life after surgery. Possible positive predictive factors to consider include preoperative psychosocial status and expectations. CLINICAL QUESTION/LEVEL OF EVIDENCE: Risk, II.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".