COST ANALYSIS OF INTRA PROCEDURAL RAPID ON SITE EVALUATION OF CYTOPATHOLOGY WITH ENDOBRONCHIAL ULTRASOUND
Bibliographic record
Abstract
BACKGROUND: Rapid on site evaluation (ROSE) allows immediate processing and interpretation of the aspirate in the procedural suite. It improves diagnostic yield and lowers patient care costs. There are limited data on its cost-effectiveness with endobronchial ultrasound (EBUS). METHODS: We developed an economic model with two arms, no ROSE (our current practice) and simulated ROSE. To simulate ROSE, a cytopathologist retrospectively identified the first diagnostic slide in each case. Using a decision analytic modeling technique under a hospital diagnostic unit perspective, the benefits of simulated ROSE were estimated as cost-savings. The model input was estimated from actual data, consulting experts, and the literature. The benefits were estimated as cost savings per patient and for the province of Alberta per year. Due to differences in the procedure, sarcoidosis and cancer patients were analyzed separately. The costs are shown in 2012 Canadian dollars, CAD. RESULTS: In our model without ROSE, the procedure cost/patient was CAD 646.00(USD 523.32) for cancer and CAD 1,170.00 (USD 947.73) for sarcoidosis. With simulated ROSE cost savings of CAD 63.00(37.00 to 89.00) [USD 51.04(29.97 to 72.10)], CAD 544.00(490.00 to 598.00) [USD 440.65(397.05 to 484.44)] for cancer and sarcoidosis, respectively. Extrapolating this to provincial data, our model estimates that EBUS with ROSE would lead to savings of CAD 50,000.00(30,000 to 71,000) [USD 40,501.24 (24,300.75 to 57,531.34)] for cancer and CAD 109,000.00 (87,000 to 130,000) [USD 88,337.07 (70,546.45 to 105,313.04) for sarcoidosis. CONCLUSION: The use of ROSE with EBUS is cost saving. The projected savings were CAD 50,000.00 (USD 40,501.24) and CAD 109,000.00(USD 88,337.07) in cancer and sarcoidosis, respectively, for the province of Alberta, Canada.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".