Bibliographic record
Abstract
Abstract: Ground glass opacities (GGOs) are focal areas of increased attenuation on computed tomography (CT) that can be present in numerous benign and malignant conditions. This increasingly prevalent entity poses a clinical dilemma for both diagnosis and management. The purpose of this narrative review is to present the most up-to-date evidence and address the nuanced decision making faced by surgeons in the management of GGOs. Several international guidelines on lung nodule management include GGOs; however, there is no consensus on when invasive diagnostic testing is required, which GGOs need surgical resection, or what techniques should be used. GGO localization is the first step to surgical planning for this entity, as these small, subsolid lesions are challenging to identify intraoperatively. Techniques range from widely used CT-guided percutaneous procedures to more technologically advanced methods requiring navigational bronchoscopy. Balancing lung preservation and oncologic outcomes is another evolving area; determining the efficacy of sublobar resection (i.e., wedge resection, segmentectomy) is increasingly necessary as GGOs become more prevalent in the aging population. The management of GGOs is an evolving topic. Overall, the term “ground glass opacities” likely represents a heterogeneous entity that is in many ways biologically distinct from traditional solid non-small cell lung cancers and deserves further investigation to determine the best management strategies going forward.
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 | 0.001 | 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 | 0.000 | 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".