Low-Dose Computed Tomography (LDCT) in Workers Previously Exposed to Asbestos
Bibliographic record
Abstract
OBJECTIVES: To evaluate the lungs of asymptomatic asbestos-exposed workers who were screened for lung cancer and mesothelioma using low-dose computed tomography (LDCT) for parenchymal abnormalities. METHODS: Three hundred fifteen baseline LDCT studies of the chest of participants with at least 20 years' exposure to asbestos or presence of pleural plaques before enrollment on chest radiographs were analyzed. RESULTS: Three hundred fifteen subjects were studied. The mean age was 61.7 years, and the mean exposure to asbestos was 26.9 years. One hundred seventy-five (56%) participants had absence of parenchymal findings with a mean age of 58.7 years, mean exposure of 24.6 years, and a mean smoking pack years of 19. One hundred forty subjects (44%) had parenchymal findings (138 men and 2 women) with a mean age of 65.3 years, mean exposure of 29.73 years, and a mean smoking pack years of 21.5 years. Participants who had parenchymal manifestations were more likely to be older and have longer exposure to asbestos compared to participants who had no relevant parenchymal findings. There was no statistical difference in the mean smoking pack years between the groups with and without parenchymal findings. CONCLUSIONS: Low-dose CT could demonstrate parenchymal lung manifestations in this higher-risk asymptomatic group with prior exposure to asbestos in the setting of screening for lung cancer and mesothelioma. Individuals with longer exposure to asbestos and of higher age have more pulmonary abnormalities. The age and the latency of exposure play an important role given that the asbestos-related parenchymal abnormalities on LDCT were more prevalent in the elderly participants and with longer periods of exposure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".