Assessing the Impact of Incidental Findings in a Lung Cancer Screening Study by Using Low-dose Computed Tomography
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
PURPOSE: To assess the prevalence and nature of incidental findings (IF) seen in low-dose computed tomographies (LDCT) from a lung cancer screening study for at-risk individuals. MATERIALS AND METHODS: Radiology reports from LDCTs of 4073 participants of a lung cancer screening study were retrospectively reviewed for findings other than lung nodules, that is, IFs, which were regarded as actionable. The frequency, nature, and expected cost of these IFs, and their anticipated follow-up were estimated. RESULTS: There were 880 IFs described in 782 study participants (19%); the median age of the participants was 62 years (range, 46-80 years). More IFs were found in men (55%) than in women. The majority of these findings were noncardiovascular (76%), for which imaging was suggested for 74%. There were 7 severe IFs (0.8%) that merited immediate attention. Seven known cancers were diagnosed from follow-ups of the IFs. The majority of IFs (n = 486 [55%]) would require imaging follow-up if clinically indicated, with an estimated total a cost of CAN$45,500 to CAN$51,000 to provide initial diagnostic workup. CONCLUSION: IFs on lung cancer screening studies are not uncommon and frequently require imaging or other follow-up for definitive diagnoses and to assess their clinical relevance. The implication of IFs has to be considered when determining a cost-effective and ethical protocol for the utilisation of LDCT in a high-risk population.
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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 | 0.000 | 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