A Population-based Study of Survival Impact of New Targeted and Immune-based Therapies for Metastatic or Unresectable Melanoma
Bibliographic record
Abstract
AIMS: New targeted drugs and immune therapies reported since 2010 for metastatic or unresectable melanoma (MM) have shown improved survival in randomised trials. We studied the uptake of these new drugs and their impact on population-based survival. MATERIALS AND METHODS: This was a retrospective, population-based cohort study of all patients treated for MM in Ontario 2007-2015. Provincial administrative sources covering the whole population identified palliative systemic therapy, radiotherapy and metastasis surgery. Temporal trends in utilisation and survival were investigated, as was survival of treatments predefined as 'new drugs' (BRAF or MEK inhibitors, anti-CTLA4 and anti-PD-1 antibodies). RESULTS: We identified 2793 treated MM patients. First treatment was systemic therapy (46%), radiotherapy (41%) and metastasis surgery (14%). Systemic treatment increased from 53% of patients (2007) to 75% (2015). New drug treatments increased from <6% of known first-line regimens in 2007 to 82% in 2015. One and 2 year overall survival was 28% and 15%, respectively, for all MM 2007-2009, rising to 46% and 35% for 2014-2015 (adjusted hazard ratio 0.56, 95% confidence interval 0.49-0.63, P < 0.0001). Survival gains were observed primarily among those cases initially treated with systemic therapy, which became dominated by the use of new drugs over the study period (2 year overall survival 16% 2007-2009 versus 44% 2014-2015; adjusted hazard ratio 0.46, 95% confidence interval 0.38-0.56, P < 0.0001). CONCLUSIONS: Utilisation of new targeted drugs and immune therapies for MM has increased considerably in routine practice 2007-2015. Consistent with the results of clinical trials, adoption was associated with substantial increases in survival of patients in the general population.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".