Point-of-care ultrasound training in nephrology: a position statement by the International Alliance for POCUS in Nephrology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Point-of-care ultrasonography (POCUS) has rapidly evolved from a niche technology to an indispensable tool across medical specialties, including nephrology. This evolution is driven by advancements in technology and the visionary efforts of clinicians in emergency medicine and beyond. Recognizing its potential, medical schools are increasingly integrating POCUS into training curricula, emphasizing its role in enhancing diagnostic accuracy and patient care. Despite these advancements, barriers such as limited faculty expertise and 'lack of' standardized guidelines hinder widespread adoption and regulation. The International Alliance for POCUS in Nephrology (IAPN), through this position statement, aims to guide nephrologists in harnessing the diagnostic power of POCUS responsibly and effectively. By outlining core competencies, recommending training modalities and advocating for robust quality assurance measures, we envision a future where POCUS enhances nephrology practice globally, ensuring optimal patient outcomes through informed, evidence-based decision-making. International collaboration and education are essential to overcome current challenges and realize the full potential of POCUS in nephrology and beyond.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it