Long-Term Survival in Patients with Metastatic Melanoma Treated with DTIC or Temozolomide
Bibliographic record
Abstract
BACKGROUND: Patients with metastatic melanoma typically have a poor outcome; however, a small proportion of patients achieve long-term survival (LTS). It is unclear how often LTS is related to sensitivity to chemotherapy. METHODS: All patients with metastatic melanoma treated with either dacarbazine (DTIC) or temozolomide (TMZ) at the British Columbia Cancer Agency (BCCA) from January 1, 1988 to February 1, 2006 were identified through the BCCA pharmacy electronic database, which was then linked to the surveillance and outcomes unit to identify patients with LTS, defined as survival > or =18 months following chemotherapy. RESULTS: In total, 397 patients were treated with either DTIC (n = 349) or TMZ (n = 48) and 43 patients (10.8%) were identified with LTS. Two additional patients with LTS were added prior to 1988 for a total of 45 patients. The 5-year overall and progression-free survival rates for patients with LTS were 33% and 16%, respectively. In total, 16% had a complete response (CR) to chemotherapy, which was the only factor identified that correlated with survival in the multivariate analysis. However, most patients with LTS had an incomplete response to chemotherapy. CONCLUSIONS: LTS occurs in select patients who achieve a CR to chemotherapy. However, this occurs in only a minority of patients and, in most cases, the longer survival is likely the result of indolent disease biology or host factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".