Pulmonary subsolid nodules: what radiologists need to know about the imaging features and management strategy
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
Pulmonary subsolid nodules (SSNs) refer to pulmonary nodules with pure ground-glass nodules and part-solid ground-glass nodules. SSNs are frequently encountered in the clinical setting, such as screening chest computed tomography (CT). The main concern regarding pulmonary SSNs, particularly when they are persistent, has been lung adenocarcinoma and its precursors. The CT manifestations of SSNs help radiologists and clinicians manage these lesions. However, the management plan for SSNs has not previously been standardized. Recently, the Fleischner Society published recommendations for the management of incidentally detected SSNs. The guidelines reflect the new lung adenocarcinoma classification system proposed by the International Association for the Study of Lung Cancer, American Thoracic Society, and European Respiratory Society (IASLC/ATS/ERS) and include six specific recommendations according to the nodule size, solid portion and multiplicity. This review aims to increase the understanding of SSNs and the imaging features of SSNs according to their histology, natural course, possible radiologic interventions, such as biopsy, localization prior to surgery, and current management.
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) | 0.001 | 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 | 0.000 | 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 it