What Does Cleft Lip and Palate Care Cost? The Time and Economic-Associated Burden of Care From Birth to Maturity
Bibliographic record
Abstract
Introduction: This study aims to describe the burden of care (BoC) for the management of patients with nonsyndromic cleft lip and palate (CLP) by identifying provider burden, characterizing an interaction burden, and calculating an economic burden associated with their health system interactions. Methods: A retrospective chart review was conducted of patients with nonsyndromic CLP treated at a pediatric tertiary hospital between January 1, 1999, and April 30, 2021. Healthcare utilization data for inpatient and outpatient interactions were extracted. Community outpatient data were obtained from affiliated specialists. Bottom-up microcosting was utilized for hospital costing, the provincial tariff guide for provider reimbursement, and zip code for calculating patient costs. Results: In total, 58 patients identified with CLP had a median of 148.5 healthcare interactions (consults/follow-ups/surgeries) between the ages of 0 and 18 years. Patients had a median of 10.5 surgical procedures, and a median 135.8 outpatient interactions. The most used specialty service was orthodontics, with a median of 71.5 orthodontic interactions per patient. The median cost of care, including direct hospital costs, physician costs, community healthcare costs, and indirect costs, was $73,398. Conclusions: Patients born with nonsyndromic CLP have a very high frequency of healthcare encounters, out of proportion to cost associated with healthcare, suggesting an overall significant BoC.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".