Surgeon-led Point-of-care Ultrasound-guided Thoracic Biopsy: A new paradigm in efficient diagnosis and resource-sparing care
Bibliographic record
Abstract
OBJECTIVE: Tissue diagnosis through a variety of interventional approaches guides thoracic cancer management, but often introduces delay to definitive treatment and can be resource intensive. We introduced a thoracic surgeon-led, point-of-care ultrasound-guided biopsy program to provide rapid diagnosis for patients with thoracic cancers. We assessed the diagnostic yield and adverse events with this approach. METHODS: A prospective cohort study was performed of consecutive patients undergoing ultrasound-guided biopsies performed by 5 thoracic surgeons from June 2021 to April 2024 at a tertiary Canadian thoracic surgery institution. By using a bedside ultrasound, 20-gauge tissue cores were obtained using multiple passes with a standard spinal needle. Descriptive univariable statistics were used. RESULTS: A total of 160 patients underwent bedside biopsy for lung (n = 101), liver (n = 20), chest wall/pleural (n = 20), mediastinal (n = 18), or other (n = 1) lesions. Tissue diagnosis was obtained in 86.3% of patients (n = 138), and diagnostic yield was similar for high- and low-volume providers and over time. All liver biopsies were diagnostic. Nondiagnostic biopsies were more likely to occur with benign pathology, chest wall/pleural lesions, or extensive necrosis; diagnosis was achieved with other modalities in most cases. There was 1 postprocedure pneumothorax (adverse event rate 0.6%). CONCLUSIONS: Thoracic surgeon-led ultrasound-guided biopsies are safe in an outpatient clinic setting and have high diagnostic accuracy. This results in reduced time to diagnosis by an estimated 28 to 35 days and frees up endoscopic and radiology resources for other patients. This low-cost procedure can be adopted as part of comprehensive thoracic malignancy assessment and can accelerate patient access to cancer treatment.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".