Comparing characteristics of melanoma cases arising in health maintenance organizations with state and national registries
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
Datasets from large health maintenance organizations (HMOs), particularly those with established cancer registries that report to the Surveillance, Epidemiology, and End Results program, are potentially excellent resources for studying melanoma epidemiology and outcomes. However, generalizability of the findings beyond HMO-based populations has not been well studied. We compared melanoma patient, tumor, and treatment characteristics at Kaiser Permanente Northern California and Henry Ford Healthcare Systems with those of corresponding regional, state, and national registry-reported melanoma databases. We identified all melanoma cases diagnosed at Kaiser Permanente Northern California (1996-2009) and Henry Ford Healthcare Systems (1996-2007) and ascertained patient (age, sex, race, and ethnicity), tumor (site, size, laterality, invasiveness, depth, ulceration, subtype, and stage), and treatment (surgery and radiation) variables from health system cancer registries. Registry data were obtained from Surveillance, Epidemiology, and End Results databases for the reporting period ending in November 2011. We found that melanoma cases arising in HMO settings generally have comparable patient, tumor, and treatment characteristics to regional, state, and national cases. An important difference included improved reporting of race information at HMO sites. Melanoma studies using data derived from select HMOs are potentially generalizable to local, state, and national populations, and may be better situated for studying racial-ethnic disparities.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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