A cost minimization analysis of the implementation of the international lung screening trial in Catalonia (Spain)
Bibliographic record
Abstract
BACKGROUND: NLST and NELSON trial showed that lung cancer mortality can be reduced by 20-24% using low-dose computed tomography screening, due to an increase in early-stage diagnoses. RESEARCH QUESTION: How much lung cancer-related direct costs may be reduced using low-dose computed tomography screening based on the ILST-protocol in a public healthcare system? METHODS: Cost analysis of lung cancer screening vs. usual care in the framework of the retail price of the Catalan public healthcare system. The lung cancer screening group included costs of screening (ILST-protocol), treatment cost according to weighted average distribution of TNM staging in the NLST and NELSON trials, lung cancer detection rate and smoking-cessation intervention. The usual care group included treatment costs based on distribution of TNM staging registered in the Spanish index hospital. RESULTS: In the usual care group, treatment costs were €91,959. In 5-year of lung cancer screening program, the average expected costs per subject were €1,342 (range €1,054 - 1,832) for screening and €32,431 for treatment, with an expected reduction of €952 based on an average cancer detection rate of 1.6%. The decrease in cost resulting from the stage shift offsets 70.6% of the costs of the screening program. CONCLUSIONS: The decrease in direct costs associated with lung cancer treatment due to a stage shift resulting from LCS of high-risk populations compensates for a substantial part of the LCS program costs. TRIAL REGISTRATION: Retrospectively registered.
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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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".