Tympanostomy tube insertion practice in under-18-year-olds in the South African private healthcare sector insured by Discovery Health
Bibliographic record
Abstract
BACKGROUND: The reported rates of tympanostomy tube insertion (TTI) in children vary significantly internationally. Lack of adherence to evidence-based clinical guidelines may contribute to these differences. OBJECTIVES: To study the rates of TTI in South Africa (SA) in children ≤18 years old in the private healthcare sector, both nationally and regionally, to compare these with international TTI rates, and to determine the use of preoperative audiometry and tympanometry. METHODS: A retrospective analysis was done of data obtained from the Discovery Health database. Rates of TTI were analysed nationally and regionally and in different age groups, as was the use of tympanometry and audiograms. RESULTS: The SA TTI rates were much higher than published international rates except for the 0 - 1-year age group in Canada and Denmark and the 0 - 15-year age group in Denmark. There was a statistically significant regional variation in TTI rates as well as in the use of preoperative audiometry and tympanometry. CONCLUSIONS: SA private sector TTI rates are high by international standards. Significant regional variations may indicate over- or underservicing in certain regions. Further investigation of causes for the high TTI rate and regional variations is recommended. Education of healthcare professionals on recognised indications for TTI may improve patient selection.
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 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".