Population survival impact of new targeted and immune based therapies for metastatic or unresectable melanoma
Bibliographic record
Abstract
IntroductionNew classes of drugs for metastatic or unresectable melanoma (MM) have shown improved survival in randomized trials (e.g., anti-CTLA-4, anti-PD-1, BRAF/MEK inhibitors). We sought to describe uptake of these new drugs and their impact on population-based survival outcomes of MM. Objectives and ApproachWe sought to describe uptake of these new drugs and their impact on population-based survival outcomes of MM. This was a retrospective, population-based cohort study of all treated MM in Ontario 2007-2015. Administrative data sources from the Institute for Clinical Evaluative Sciences (ICES) were utilized. Within ICES, cutaneous and non-cutaneous primaries were identified in the Ontario Cancer Registry. Administrative sources from Cancer Care Ontario, Ministry of Health and Long-Term Care, and Canadian Institute for Health Information identified patients treated with palliative systemic therapy, radiotherapy and metastatectomy. Temporal trends in utilization and survival were investigated. Survival by drug class was described. ResultsWe identified 2,793 MM patients. First treatment was systemic therapy (46%), radiotherapy (41%) or metastatectomy (14%). MM patient number increased from 270 in 2007 to 418 in 2015. Systemic treatment rose from 125 MM first treated in 2007 to 343 in 2015. New drug treatments increased from <6% of reported first-line regimens in 2007 to 82\% in 2015. 1-year and 2-year overall survival (OS) was 28% and 15% respectively for all MM in 2007-2009, rising to 46% and 35% for 2014-2015 (logrank p<0.001; adjusted hazard ratio (AHR) 0.56, 95% confidence interval (CI): (0.49,0.63)). Survival gains were largely in the subset treated primarily systemically, where new drugs were increasingly utilized (2-year OS 16% 2007-2009 vs. 44% 2014-2015 logrank p<0.001; AHR 0.46, 95% CI: (0.38,0.56)). Conclusion/ImplicationsUtilization of systemic therapy for MM has increased considerably in routine practice during 2007-2015; at least some of this increase relates to use of novel agents since 2011. In line with randomized trial findings, new drug adoption was associated with substantial increases in population-based MM survival.
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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".