Preoperative Characteristics and Postoperative Outcomes Following Adenoidectomy in Children
Bibliographic record
Abstract
OBJECTIVES: (1) To describe various preoperative and diagnostic findings of children undergoing adenoidectomy and (2) to evaluate quality of life outcomes following adenoidectomy in children. METHODS: This was a prospective observational study. Patients who were candidates for adenoidectomy at The Hospital for Sick Children were evaluated with respect to preoperative symptoms, flexible nasal endoscopy (FNE) findings, and nasal airflow (NAF) studies. Following surgery, a quality of life questionnaire was administered to all available patients and an attempt was made to repeat the NAF study. RESULTS: Fifty-seven patients were included in the study (31 females and 26 males). The average age of the patient at surgery was 10.1 years. Thirty patients (52.6%) had a significant NAF study, whereas FNE revealed an average nasopharyngeal obstruction of 73.2%. We did not find any correlation between preoperative symptoms, FNE findings, or NAF study results. The degree of symptom reduction was the only predictor of how satisfied a patient would be in the postoperative period (p<.05). CONCLUSION: In our study of adenoidectomy patients, clinical signs and symptoms appear to be more predictable than the objective tests of NAF and FNE. As such, following these symptoms in the postoperative period is important in determining a patient's satisfaction following 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 | 0.000 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".