Burden of Merkel cell carcinoma: A targeted literature review.
Bibliographic record
Abstract
e21073 Background: Merkel cell carcinoma (MCC) is a rare, aggressive skin cancer characterized by high incidence of local recurrences, regional nodal metastases, distant metastases, and high mortality rates. It has no approved drug therapies and limited treatment options, especially to manage the metastatic disease. Our objective was to conduct a comprehensive review of MCC disease burden. Methods: We conducted a targeted literature review of studies published from 2010 to 2016 to assess epidemiology, patient burden, and unmet needs associated with MCC. An article was retrieved for full review if the abstract met each of the following criteria: reported incidence, natural morbidity or mortality of MCC; derived from a peer-reviewed journal; and reported in English-language published since January 2010. Results: The incidence of MCC (per 100,000 persons per year) is reported highest in Australia (0.82-1.60), followed by the US (0.6), Netherlands (0.35), Sweden (0.18-0.33), Finland (0.24-0.25), Spain (0.28), Denmark (0.22), South East Scotland (0.133), France (0.13). Inadequate data available from countries like Canada, UK, Germany and most part of Asia-Pacific. Typically, stage I-II MCC is observed in approximately 60% - 75% patients, stage III in 10% - 30% patients, and stage IV (metastatic) in 2% - 16% patients. The 5-year and 10-year survival is 0% - 68%, and 21% - 65% respectively, based on factors such as disease stage and gender. Although less than 20% patients progress to advanced metastatic stage IV MCC, they experience the highest unmet need due to lack of effective therapeutic options. Data on economic burden and humanistic burden (e.g., impact on patient quality of life) is not available. Conclusions: Epidemiological review indicates an increasing incidence of MCC; however, available estimates are at least 5 years old. Epidemiological data as well as data on clinical, economic and humanistic burden of MCC is lacking from several parts of the world. Further research is warranted to adequately quantify the burden-of-illness of MCC and assess comparative effectiveness of evolving treatment options to better inform patients, prescribers and payer organizations concerning optimal cost-effective modalities of disease management.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".