Screening for lung cancer using low dose computed tomography
Bibliographic record
Abstract
Screening for lung cancer with low dose computed tomography can reduce mortality from the disease by 20% in high risk smokers. This review covers the state of the art knowledge on several aspects of implementing a screening program. The most important are to identify people who are at high enough risk to warrant screening and the appropriate management of lung nodules found at screening. An accurate risk prediction model is more efficient than age and pack years of smoking alone at identifying those who will develop lung cancer and die from the disease. Algorithms are available for assessing people who screen positive to determine who needs additional imaging or invasive investigations. Concerns about low dose computed tomography screening include false positive results, overdiagnosis, radiation exposure, and costs. Further work is needed to define the frequency and duration of screening and to refine risk prediction models so that they can be used to assess the risk of lung cancer in special populations. Another important area is the use of computer vision software tools to facilitate high throughput interpretation of low dose computed tomography images so that costs can be reduced and the consistency of scan interpretation can be improved. Sufficient data are available to support the implementation of screening programs at the population level in stages that can be expanded when found to perform well to improve the outcome of patients with lung cancer.
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.
How this classification was reachedexpand
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) | 0.002 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".