Canadian Internal Medicine Ultrasound (CIMUS) Recommendations Regarding Internal Medicine Point-of-Care Ultrasound (POCUS) use during Coronavirus (COVID-19) pandemic.
Bibliographic record
Abstract
With the COVID-19 pandemic, we are in unprecedented times - our clinical environment is changing rapidly and may continue to do so in the future. Over the last decade there has been an increased support for the use of internal medicine point-of-care ultrasound (POCUS) across the country and worldwide. While standard infection control guidelines are available on device and tranducer cleaning and disinfection, these recommendations may not apply during the COVID-19 pandemic. While we anticipate that the experience and need for POCUS deployment will differ across the country depending on several contextual factors, similar principles will likely emerge across multiple settings. To that end, to enable POCUS readiness, we recommend that each program/ practice site consider undertaking the following steps and recommendations on a semi-urgent basis if POCUS use is anticipated. The objective of this article to provide internists who currently use POCUS with the interim recommendations on processes that need to be in place prior to its use. This document refers primarily to the non-critical use of ultrasound devices based on the Spaulding classification6 (see Appendix for definitions) and does not apply to the setting of critical use where sterilization is required, nor semi-critical use, where high-level disinfection is required. Each institution must have its own policy in place on the cleaning and disinfection procedures for POCUS. This doucument is meant to serve as an adjunct to existing protocols.
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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 | 0.012 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".