Lung POCUS in a Pulmonary Outpatient Clinic: Balancing Utility and Feasibility
Bibliographic record
Abstract
Background: Despite abundant literature supporting the diagnostic utility of lung point of care ultrasound (POCUS) in inpatient settings, there is limited data on the feasibility and utility of lung POCUS in pulmonary outpatient clinics. Patients may first seek care for dyspnea in a pulmonary outpatient clinic. Existing data on in-hospital use suggests that integrating lung POCUS into clinic visits may lead to earlier diagnosis and limit the need for additional testing [1,2]. Objective: The aim of this observational study was to evaluate the feasibility, clinical impact, indications, practices, and findings associated with lung POCUS in an urban-based pulmonary outpatient clinic. Methods: We reviewed 100 consecutive patients who underwent a lung POCUS exam during their pulmonary outpatient clinic visit. Lung POCUS was performed by trainees and faculty, stored in a cloud-based archival system, and reviewed by pulmonary attendings. Findings were categorized into three patterns: predominant A-line, predominant B-lines, and predominant A-lines with a B-line focus. Summary statistics were performed using the data. Results: The mean age of the included patients was 57+/- 17 years, and 64% were female. Residents performed 70% of the studies. The median time for a lung POCUS exam was 5 minutes. A normal lung POCUS with a predominant bilateral A-line pattern (71%) was most commonly associated with obstructive airway disease (31%). A bilateral B-line pattern (13%) was associated with either interstitial lung disease (7%) or heart failure (8%). Focal dense B-lines (10%) were seen with atelectasis (3%) or other abnormal computed tomography (CT) findings (3%). Lung POCUS demonstrated stability versus progression of pleural effusions in 18% of cases. Of the 49% of patients who had additional imaging ordered for them, there was 100% concordance between lung POCUS findings and chest X-ray (CXR). We explored the potential impact of lung POCUS on clinical management in five cases. Conclusions: Lung POCUS is feasible to perform in a pulmonary outpatient clinic without adding a significant amount of time to the patient encounter. There is strong concordance between a normal lung POCUS and CXR, which can supplant the need for CXRs in certain conditions. Areas of future research include evaluating providers' attitudes towards lung POCUS use in the clinic setting and integrating lung POCUS into clinic workflow.
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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 | 0.001 | 0.000 |
| 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 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".