The Surveillance, Epidemiology, and End Results (SEER) Program and Pathology
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
The Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute collects data on cancer diagnoses, treatment, and survival for approximately 30% of the United States (US) population. To reflect advances in research and oncology practice, approaches to cancer control are evolving from simply enumerating the development of cancers by organ site in populations to including monitoring of cancer occurrence by histopathologic and molecular subtype, as defined by driver mutations and other alterations. SEER is an important population-based resource for understanding the implications of pathology diagnoses across demographic groups, geographic regions, and time and provides unique insights into the practice of oncology in the US that are not attainable from other sources. It provides incidence, survival, and mortality data for histopathologic cancer subtypes, and data by molecular subtyping are expanding. The program is developing systems to capture additional biomarker data, results from special populations, and expand biospecimen banking to enable cutting-edge cancer research and oncology practice. Pathology has always been central and critical to the effectiveness of SEER, and strengthening this relationship in this modern era of cancer diagnosis could be mutually beneficial. Achieving this goal requires close interactions between pathologists and the SEER program. This review provides a brief overview of SEER, focuses on facets relevant to pathology practice and research, and highlights the opportunities and challenges for pathologists to benefit from and enhance the value of SEER data.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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