Does Real-World Evidence of the Economic Burden of Lung Cancer in Greece Exist? A Systematic Review of the Literature
Bibliographic record
Abstract
OBJECTIVE: This systematic literature review aimed to summarize the economic burden of lung cancer in Greece, identify current data gaps, and support the design of future real-world studies. METHODS: A systematic search of studies published in English on the cost of lung cancer was performed in MEDLINE-(PubMed), Scopus, and ScienceDirect. The databases were searched until September 2024, and records were screened based on our eligibility criteria. After conducting the initial literature search, the abstracts and full texts of the identified studies were reviewed and evaluated for inclusion based on predefined criteria. Data from the selected studies were then extracted into a standardized form and subsequently synthesized. RESULTS: Seven studies were included in this review. The reported burden was sourced from hospital data and categorized as direct and indirect costs. Most studies (n = 6) reported direct costs, with one study reporting both direct and indirect costs. The total direct medical cost per patient increased from approximately EUR 16,000 in 2015 to EUR 58,974 in 2023, with drug acquisition costs being the key driver of the total direct cost. Additionally, the cost of end-of-life care during the final six months of a patient's life was estimated to range from EUR 6786 to EUR 7665 per patient, with pharmaceutical costs comprising the largest proportion of the total cost. One study also reported that indirect costs were considerably higher for patients than for family caregivers. CONCLUSION: The economic burden of lung cancer has increased substantially over the past decade in Greece. The present systematic review emphasizes the critical need for comprehensive real-world studies on the economic burden of lung cancer in Greece. Addressing the current gaps holistically will yield invaluable insights for policymakers and stakeholders.
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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 | 0.016 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| 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".