Surgical applications of ultrasound use in low‐ and <scp>middle‐income</scp> countries: A systematic review
Bibliographic record
Abstract
Background: Ultrasound is a portable technology able to deploy health care effectively in low resource settings. This study presents a systematic review to determine trends in the utility and applicability of this technology in low- and middle-income countries (LMIC), specifically for surgical applications. The review includes characterising and evaluating trends in the geographic and specialty-specific use of ultrasound pertaining to surgical disease. Methods: The databases such as Medline OVID, EMBASE and Cochrane were searched from 2010 through March 2019 for studies available in English, French and Spanish. Commentaries, opinion articles, reviews and book chapters were excluded. A categorical analysis of ultrasound use for surgical disease in LMICs was conducted. Results: A total of 6276 articles were identified, with 4563 studies included for the final review. A total of 221 studies were selected researching ultrasound use in LMICs to treat surgical disease. Most studies identified ultrasound usage focused on general surgery, acute care surgery and surgical ICU topics (52%, 115) followed by computed tomography surgery studies (20%, 44). Most studies were retrospective in nature, with 81% (180) of research studies generated in four countries (India, Pakistan, Nigeria and Egypt). Ultrasound proved to be a feasible technique for utility in pre-operative diagnosis, cost-effectiveness and prediction of surgical outcomes. Findings are limited by the limited number of randomised clinical trials reported. Conclusion and global health implications: Our systematic literature review of ultrasound use in LMICs demonstrates the growing utilisation of this relatively low-cost, portable imaging technology in low resource settings for surgical disease.
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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 | 0.008 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".