Pleural ultrasound as an adjunct to physical examination in the preoperative evaluation of lung cancer patients
Bibliographic record
Abstract
OBJECTIVES: Preoperative evaluation of patients with suspected or confirmed lung cancer consists of clinical and radiological staging. Malignant pleural effusion is a poor prognosticator in non-small-cell lung cancer. Pleural ultrasound (PU) allows for the assessment of pleural effusion, providing real-time guidance for its aspiration and cytological analysis. Pleural Ultrasonography in Lung Cancer (PULC) as an adjunct to physical examination has the potential to improve preoperative staging of non-small-cell lung cancer during first surgical encounter by allowing the evaluation of previously unassessed pleural effusion. METHODS: This study consisted of a prospective trial of surgeon-performed PU in the preoperative evaluation of lung cancer patients. All patients evaluated in the thoracic surgery clinic with the new or presumed diagnosis of lung cancer were eligible. A portable ultrasound machine was used to evaluate pleural fluid in the bilateral costophrenic sulci with pleural fluid aspiration for cytological analysis. RESULTS: Forty-five patients were prospectively enrolled over a 3-month period. Thirteen patients had ultrasound evidence of a pleural effusion, of which 3 were significant enough for aspiration. Cytological analysis of these effusions yielded malignant cells in 1 patient. Positive PULC evaluation led to a change in clinical staging (M0 to M1a) in 10 patients and a change in pathological staging (pleural fluid cytology positive) in 1 patient. The time required for PULC examination was 15 ± 7 min. There were no complications related to the procedures. CONCLUSIONS: Preoperative pleural ultrasonography is a rapid and effective way to improve precision of staging in patients with lung cancer. More precise staging may allow for more appropriate testing, patient prognostication and operative planning.
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 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 | 0.002 | 0.004 |
| 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) | 0.001 | 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".