3M™ Defib-Pads as a Reusable Alternative to Commercial Ultrasound Gel in Resource-Limited Settings
Bibliographic record
Abstract
INTRODUCTION: With point-of-care ultrasound (POCUS) use in austere environments comes the challenge of having an ever-available coupling medium for image generation. Commercial gel has numerous drawbacks that can limit its utility in these settings, and no studies have evaluated the potential for a reusable coupling medium. This study aimed to determine whether 3M™ Defib-Pads could be utilized as a reusable alternative to commercial gel for image generation in resource-limited settings. METHODS: A descriptive, cross-sectional survey of Canadian physicians with POCUS interest was conducted to evaluate the interpretability of various POCUS images in a blinded fashion. Three anatomic regions (cardiac, abdominal, and nerve) were utilized, and image generation from the commercial gel and 7 Defib-Pad conditions were evaluated. These included pads that were 1) newly opened, 2) dirtied then rinsed, 3) air dried, 4) rinsed after being air dried, 5) frozen then thawed, 6) used in double thickness, and 7) used with a probe cover. RESULTS: Compared to commercial gel, 3M™ Defib-Pads performed similarly, with adequate image interpretability of up to 100% in some conditions. The exception was pads that had prolonged air exposure, which produced images that were never interpretable. However, subsequent rinsing of these pads with water resulted in restored image generation. CONCLUSION: 3M™ Defib-Pads were found to produce interpretable POCUS images under multiple environmental stressors and with different modalities of use, suggesting that 3M™ Defib-Pads can perform as a reusable gel alternative in resource-limited settings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".