Impact of Patient Socioeconomic Disparities on Time to Tympanostomy Tube Placement
Bibliographic record
Abstract
Objectives: Extensive literature exists documenting disparities in access to healthcare for patients with lower socioeconomic status (SES). The objective of this study was to examine access disparities and differences in surgical wait times in children with the most common pediatric otolaryngologic surgery, tympanostomy tubes (TT). Methods: A retrospective cohort study was performed at a tertiary children’s hospital. Children ages <18 years who received a first set of tympanostomy tubes during 2015 were studied. Patient demographics and markers of SES including zip code, health insurance type, and appointment no-shows were recorded. Clinical measures included risk factors, symptoms, and age at presentation and first TT. Results: A total of 969 patients were included. Average age at surgery was 2.11 years. Almost 90% were white and 67.5% had private insurance. Patients with public insurance, ≥1 no-show appointment, and who lived in zip codes with the median income below the United States median had a longer period from otologic consult and preoperative clinic to TT, but no differences were seen in race. Those with public insurance had their surgery at an older age than those with private insurance ( P < .001) and were more likely to have chronic otitis media with effusion as their indication for surgery (OR: 1.8, 95% CI: 1.2-2.5, P = .003). Conclusions: Lower SES is associated with chronic otitis media with effusion and a longer wait time from otologic consult and preoperative clinic to TT placement. By being transparent in socioeconomic disparities, we can begin to expose systemic problems and move forward with interventions. Level of Evidence: 4
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".