Point of care ultrasound (PoCUS) in low- and middle-income countries
Bibliographic record
Abstract
Point-of-Care Ultrasound (PoCUS) is a portable, affordable, and versatile diagnostic and procedural tool that enhances bedside decision-making. Its simplicity and safety make it especially valuable in low- and middle-income countries (LMICs), where access to advanced imaging is limited. PoCUS helps bridge diagnostic gaps by enabling real-time, noninvasive assessment and procedural guidance across anesthesiology, perioperative care, critical care, and emergency-medicine. However, implementation in LMICs faces barriers such as limited device availability, high costs, maintenance difficulties, and insufficient training. Equipment availability, accessibility and structured education remain key determinants of adoption. Training programs are often short-term and externally led, with limited long-term integration into local systems. Emerging strategies including “train-the-trainer” approaches, blended learning, and tele-mentoring—offer scalable solutions. Coordinated efforts across access, education, and sustainability—supported by mentorship and standardized credentialing—are essential. Ultimately, PoCUS represents more than a diagnostic tool; it is a driver of equity, safety, and empowerment in global health.
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".