Estimating cancer prevalence using mixture models for cancer survival
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
Knowledge of cancer prevalence is useful for estimating the ongoing level of resources utilized in the treatment of disease and is of some public health interest. Cancer prevalence is estimated first as the proportion of persons previously diagnosed (PD) with cancer that are still alive; and second as the proportion of individuals in the population who were previously diagnosed with cancer and who have not been cured (NC). The proportion of cases that are cured is estimated by assuming that the cured and uncured cases have distinct survival patterns. The hazard for cured cases is assumed to be a multiple of the hazard from causes other than cancer in the general population. The hazard for uncured cases is assumed to have two independent components: one corresponding to the disease-specific hazard, and the other a multiple of the population hazard from 'other causes'. Future prevalence estimates are obtained by projecting the survival of current prevalent cases as well as the survival of future incident cases.
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 | 0.001 | 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 | 0.001 | 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