Comparing characteristics of melanoma cases arising in health maintenance organizations with state and national registries
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.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".