Management of Lung Cancer in the Patient with Interstitial Lung Disease
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
Patients with interstitial lung disease (ILD), especially those with pulmonary fibrosis, are at increased risk of developing lung cancer. Management of lung cancer in patients with ILD is particularly challenging. Diagnosis can be complicated by difficulty differentiating lung nodules from areas of focal fibrosis, and percutaneous biopsy approaches confer an increased risk of complications in those with pulmonary fibrosis. Lung cancer treatment in these patients pose several specific considerations. The degree of lung function impairment may preclude lobectomy or surgical resection of any type. Surgical resection can trigger an acute exacerbation of the underlying ILD. The presence of ILD confers an increased risk of pneumonitis with radiotherapy, and many of the systemic therapies also carry an increased risk of pneumonitis in this population. The safety of immunotherapy in the setting of ILD remains to be fully elucidated and concerns remain as to triggering pneumonitis. The purpose of this review is to summarize the evidence regarding consideration for tissue diagnosis, chemotherapy and immunotherapy, radiotherapy, and surgery, in this patient population and discuss emerging areas of research. We also propose a multidisciplinary approach and practical considerations for monitoring for ILD progression during lung cancer treatment.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it