Recurrence and mortality rates with different treatment approaches of Merkel cell carcinoma: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Comprehensive treatment recommendations for Merkel cell carcinoma are complex. We aimed to systematically review the published data on recurrence and mortality rates associated with various treatment approaches for Merkel cell carcinoma. METHODS: Search of MEDLINE, Embase, Web of Science, and Scopus from inception to August 2015. Studies were included that reported comparative survival and recurrence data for two or more treatment modalities. Two reviewers independently reviewed and abstracted recurrence and mortality rates. Event rates for individual treatment arms in each study were pooled and meta-analyzed across studies using a random-effects model. RESULTS: Fifty-two retrospective studies met inclusion criteria, revealing a total of 1,804 patients with primary Merkel cell carcinoma with data available for analyses. The recurrence rate was higher for surgery alone (55.0%) versus a combination of surgery and radiotherapy (39.0%) (odds ratio, 2.089; 95% CI, 1.374-3.177; P < 0.001). Combination therapy including surgery, radiotherapy, and chemotherapy had a higher mortality rate (44.6%) than did combined surgery and radiotherapy (23.2%) (odds ratio, 2.688; 95% CI, 1.196-6.037; P = 0.02). CONCLUSIONS: The treatment of Merkel cell carcinoma with surgery plus adjuvant radiotherapy may produce lower recurrence rates.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.027 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".