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
Low-dose computed tomography screening in older patients with a heavy-smoking history can be viewed as an opportunity to screen for smoking-related illnesses and not just for lung cancer. Within the National Lung Screening Trial, 24.1% of all deaths were attributed to lung cancer, but there were significant competing causes of mortality in this patient population. Cardiovascular illness caused 24.8% of deaths. Other neoplasms were listed as the cause of death in 22.3%, and respiratory illness was the cause of death in 10.4%. All of these illnesses might be attributed to smoking. Low-dose computed tomography of the thorax may provide information about these diseases, which could be used to guide therapeutic intervention and, hopefully, alter the courses of these diseases. Information about coronary artery calcification, chronic obstructive pulmonary disease, and potential extrapulmonary malignancy should be provided in the report of the screening examination. This must be balanced against the risk of the burden of false-positive findings and the costs, both psychological and financial, associated with additional investigative evaluations.
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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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