Sample Size of Trials Investigating the Impact of Point‐of‐Care Ultrasound‐Guided Strategies on Patient Outcomes
Bibliographic record
Abstract
Point-of-care ultrasound (POCUS) is increasingly utilized for bedside diagnosis and management in diverse clinical contexts. However, the design of randomized controlled trials (RCTs) evaluating the impact of POCUS-guided strategies on clinical outcomes presents significant challenges. This study aims to explore the assumptions underlying sample size estimation in POCUS-guided trials and assess the adequacy of sample sizes in published trials through a systematic review. We performed a sample size analysis considering varying rates of POCUS-induced management changes and plausible effect sizes on binary and continuous patient-centered outcomes. Additionally, a systematic review of PubMed was conducted to identify RCTs comparing POCUS-guided management to usual care, extracting data on planned and actual sample sizes and justifications for sample size decisions. Sample size estimations revealed a substantial dependence on the proportion of participants experiencing management changes due to POCUS findings. For example, achieving adequate power in a trial with a moderate effect size requires over 1000 participants if POCUS alters management in 50% of cases. Our review included 25 RCTs, with a median sample size of 206 participants (interquartile range 122-250). Only 68% of trials reported sample size justifications, and 41% failed to meet planned recruitment targets, primarily due to recruitment challenges and other logistical barriers. Most trials investigating POCUS-guided strategies are underpowered, underscoring the need for realistic sample size estimations that consider the rate of POCUS-induced management changes and anticipated effect sizes. Future trials should incorporate pilot phases and innovative designs to optimize feasibility and power.
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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 | 0.332 | 0.619 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.017 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".