Merkel cell carcinoma: Changing incidence trends
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to define the incidence trends of Merkel cell carcinoma (MCC), a rare and aggressive cutaneous malignancy. MATERIALS AND METHODS: All cases of MCC of the skin between 1986 and 2001 were identified using the surveillance, epidemiology, and end results (SEER) program. Overall age-adjusted, gender-specific, age-specific, stage-specific, and regional incidence rates were calculated. All rates are per 100,000 and age-adjusted to the 2000 US standard population. Estimated annual percent change (EAPC) was calculated using a linear least squares model. RESULTS: A total of 1,124 cases of MCC were identified in the SEER registries. The rate of MCC increased from 0.15 cases per 100,000 in 1986 to 0.44 cases per 100,000 in 2001. The EAPC for the time period was 8.08%. This was statistically significant (95% CI: 6.29, 9.90, P-value < 0.05). Age-specific incidence (5-year age groups) were highest in the elderly, 4.28 per 100,000 in the 85+ age group. CONCLUSIONS: MCC incidence rates have increased threefold over the 1986-2001 period. Rates are highest in the elderly population. Further etiologic studies and identification of high-risk populations are warranted.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it