Recommendations on the Use of Ultrasound Guidance for Adult Thoracentesis: A Position Statement of the Society of Hospital Medicine
Bibliographic record
Abstract
Executive Summary: 1) We recommend that ultrasound should be used to guide thoracentesis to reduce the risk of complications, the most common being pneumothorax. 2) We recommend that ultrasound guidance should be used to increase the success rate of thoracentesis. 3) We recommend that ultrasound-guided thoracentesis should be performed or closely supervised by experienced operators. 4) We suggest that ultrasound guidance be used to reduce the risk of complications from thoracentesis in mechanically ventilated patients. 5) We recommend that ultrasound should be used to identify the chest wall, pleura, diaphragm, lung, and subdiaphragmatic organs throughout the respiratory cycle before selecting a needle insertion site. 6) We recommend that ultrasound should be used to detect the presence or absence of an effusion and approximate the volume of pleural fluid to guide clinical decision-making. 7) We recommend that ultrasound should be used to detect complex sonographic features, such as septations, to guide clinical decision-making regarding the timing and method of pleural drainage. 8) We suggest that ultrasound be used to measure the depth from the skin surface to the parietal pleura to help select an appropriate length needle and determine the maximum needle insertion depth. 9) We suggest that ultrasound be used to evaluate normal lung sliding pre- and postprocedure to rule out pneumothorax. 10) We suggest avoiding delay or interval change in patient position from the time of marking the needle insertion site to performing the thoracentesis. 11) We recommend against performing routine postprocedure chest radiographs in patients who have undergone thoracentesis successfully with ultrasound guidance and are asymptomatic with normal lung sliding postprocedure. 12) We recommend that novices who use ultrasound guidance for thoracentesis should receive focused training in lung and pleural ultrasonography and hands-on practice in procedural technique. 13) We suggest that novices undergo simulation-based training prior to performing ultrasound-guided thoracentesis on patients. 14) Learning curves for novices to become competent in lung ultrasound and ultrasound-guided thoracentesis are not completely understood, and we recommend that training should be tailored to the skill acquisition of the learner and the resources of the institution.
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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 | 0.023 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.024 | 0.020 |
| Insufficient payload (model declined to judge) | 0.015 | 0.022 |
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".