The impact of septoplasty on health‐related quality of life in paediatric patients
Bibliographic record
Abstract
OBJECTIVES: To assess the impact that septoplasty had on health-related quality of life (HRQoL) in paediatric patients and to determine whether there were patient characteristics that predicted better outcomes. DESIGN: Retrospective cohort study. SETTING: Academic paediatric otolaryngology practice. PARTICIPANTS: All paediatric patients who underwent septoplasty during the study period. MAIN OUTCOME MEASURES: The current HRQoL was assessed using the Paediatric Quality of Life Inventory (PedsQL). The Glasgow Children's Benefit Inventory (GCBI) was used to evaluate the perceived change in HRQoL following septoplasty. RESULTS: A total of 29 patients (16 boys, mean age 13 years) and their caregivers responded (response rate of 72.5%). There was a statistically significant improvement in HRQoL following septoplasty, as demonstrated by the positive mean GCBI subscores and the total GCBI score (35.1, sd = 28.4). The total mean PedsQL score for child self-report was 95.2 (sd = 6.9) and for parent-proxy report was 91.8 (sd = 8.6), which indicated good current HRQoL. The enhancement in HRQoL post-septoplasty was moderately correlated with self-reported degree of nasal obstruction pre-septoplasty (r = 0.621 for total GCBI). Also, there were differences in GCBI scores between the groups of children who wanted to have the surgery versus those who did not want to have the surgery. CONCLUSIONS: There was a significant positive change in HRQoL following paediatric septoplasty in our study population. Children who reported more severe nasal obstruction and those who wanted to have the surgery were more likely to experience enhancement of HRQoL following their surgery.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".