Cost-effectiveness analysis of clinic-based chloral hydrate sedation versus general anaesthesia for paediatric ophthalmological procedures
Bibliographic record
Abstract
BACKGROUND/AIMS: The inability of some children to tolerate detailed eye examinations often necessitates general anaesthesia (GA). The objective was to assess the incremental cost effectiveness of paediatric eye examinations carried out in an outpatient sedation unit compared with GA. METHODS: An episode of care cost-effectiveness analysis was conducted from a societal perspective. Model inputs were based on a retrospective cross-over cohort of Canadian children aged <7 years who had both an examination under sedation (EUS) and examination under anaesthesia (EUA) within an 8-month period. Costs ($CAN), adverse events and number of successful procedures were modelled in a decision analysis with one-way and probabilistic sensitivity analysis. RESULTS: The mean cost per patient was $406 (95% CI $401 to $411) for EUS and $1135 (95% CI $1125 to $1145) for EUA. The mean number of successful procedures per patient was 1.39 (95% CI 1.34 to 1.42) for EUS and 2.06 (95% CI 2.02 to 2.11) for EUA. EUA was $729 more costly on average than EUS (95% CI $719 to $738) but resulted in an additional 0.68 successful procedures per child. The result was robust to varying the cost assumptions. CONCLUSIONS: Cross-over designs offer a powerful way to assess costs and effectiveness of two interventions because patients serve as their own control. This study demonstrated significant savings when ophthalmological exams were carried out in a hospital outpatient clinic, although with slightly fewer procedures completed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".