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
OBJECTIVE: We propose three concepts of sensitivity in cancer screening and apply to data on prostate cancer. Conceptual entities: Sensitivity is the indicator on the ability of screening to find cancer in the detectable preclinical phase (DPCP). The ability is usually specified as to the screening test. We call this entity the test sensitivity. Test positivity with histological confirmation refers to the full diagnostic process and we call the corresponding entity as episode sensitivity. Ultimately, a screening programme identifies a proportion of cancers in the DPCP in the total target population, that we call programme sensitivity. We derive the formulae for these three sensitivities consistent with the incidence method. EXAMPLE: Our example on estimation of the three sensitivities is from a randomized screening trial for prostate cancer in Finland. The estimates by incidence method were substantially different, 85% for test sensitivity, 48% for episode sensitivity and 36% for programme sensitivity. CONCLUSION: More than one concept of sensitivity with standard method of estimation is needed to describe the ability of screening to identify the disease in the DPCP.
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 | 0.000 |
| 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 | 0.001 |
| 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