Variation in Quality of Tonsillectomy Perioperative Care and Revisit Rates in Children’s Hospitals
Bibliographic record
Abstract
OBJECTIVE: To describe the quality of care for routine tonsillectomy at US children's hospitals. METHODS: We conducted a retrospective cohort study of low-risk children undergoing same-day tonsillectomy between 2004 and 2010 at 36 US children's hospitals that submit data to the Pediatric Health Information System Database. We assessed quality of care by measuring evidence-based processes suggested by national guidelines, perioperative dexamethasone and no antibiotic use, and outcomes, 30-day tonsillectomy-related revisits to hospital. RESULTS: Of 139,715 children who underwent same-day tonsillectomy, 10,868 (7.8%) had a 30-day revisit to hospital. There was significant variability in the administration of dexamethasone (median 76.2%, range 0.3%-98.8%) and antibiotics (median 16.3%, range 2.7%-92.6%) across hospitals. The most common reasons for revisits were bleeding (3.0%) and vomiting and dehydration (2.2%). Older age (10-18 vs 1-3 years) was associated with a greater standardized risk of revisits for bleeding and a lower standardized risk of revisits for vomiting and dehydration. After standardizing for differences in patients and year of surgery, there was significant variability (P < .001) across hospitals in total revisits (median 7.8%, range 3.0%-12.6%), revisits for bleeding (median 3.0%, range 1.0%-8.8%), and revisits for vomiting and dehydration (median 1.9%, range 0.3%-4.4%). CONCLUSIONS: Substantial variation exists in the quality of care for routine tonsillectomy across US children's hospitals as measured by perioperative dexamethasone and antibiotic use and revisits to hospital. These data on evidence-based processes and relevant patient outcomes should be useful for hospitals' tonsillectomy quality improvement efforts.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".