Evolution of Diagnoses, Survival, and Costs of Oncological Medical Treatment for Non-Small-Cell Lung Cancer over 20 Years in Osona, Catalonia
Bibliographic record
Abstract
Non-small-cell lung cancer (NSCLC) has experienced several diagnostic and therapeutic changes over the past two decades. However, there are few studies conducted with real-world data regarding the evolution of the cost of these new drugs and the corresponding changes in the survival of these patients. We collected data on patients diagnosed with NSCLC from the tumor registry of the University Hospital of Vic from 2002 to 2021. We analyzed the epidemiological and pathological characteristics of these patients, the diverse oncological treatments administered, and the survival outcomes extending at least 18 months post-diagnosis. We also collected data on pharmacological costs, aligning them with the treatments received by each patient to determine the cost associated with individualized treatments. Our study included 905 patients diagnosed with NSCLC. We observed a dynamic shift in histopathological subtypes from squamous carcinoma in the initial years to adenocarcinoma. Regarding the treatment approach, the use of chemotherapy declined over time, replaced by immunotherapy, while molecular therapy showed relative stability. An increase in survival at 18 months after diagnosis was observed in patients with advanced stages over the most recent years of this study, along with the advent of immunotherapy. Mean treatment costs per patient ranged from EUR 1413.16 to EUR 22,029.87 and reached a peak of EUR 48,283.80 in 2017 after the advent of immunotherapy. This retrospective study, based on real-world data, documents the evolution of pathological characteristics, survival rates, and medical treatment costs for NSCLC over the last two decades. After the introduction of immunotherapy, patients in advanced stages showed an improvement in survival at 18 months, coupled with an increase in treatment costs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".