Evaluation of Interventions Addressing Timely Access to Surgical Care in Low‐Income and Low‐Middle‐Income Countries as Outlined by the LANCET Commission 2030 Global Surgery Goals: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: In 2015, the Lancet Commission on Global Surgery published six global surgery goals, one of which was to provide 80% of the world's population with timely access to the Bellwether Surgical procedures. Little is known about the prevalence or efficacy of subsequent interventions implemented in under-resourced countries to increase timely access to Bellwether surgical procedures. METHODS: A systematic review of articles and grey literature published in MEDLINE, Embase, Cochrane, CINAHL, and Web of Science databases was conducted. Two independent reviewers evaluated 1923 captured abstracts using explicit inclusion and exclusion criteria. Following a thematic analysis, two reviewers conducted data extraction on the eleven manuscripts included in the final review. RESULTS: The studied innovations, sparse in number, centred on improved educational resources, the development of orthopaedic devices, and models for assessing surgical access disparity. Eight papers were centred around timely access to caesarean sections, three around open fracture reduction, and three around laparotomy; all focused on adult populations. Five papers addressed innovations in West Africa, two in East Africa, two in South Asia, and one in Southeast Asia. Common outcome metrics were not used to assess improvements to timely surgical access. CONCLUSIONS: Few published interventions have been implemented since the publication of the 2015 Lancet Commission on Global Surgery goals that have or will longitudinally increase the availability of timely surgical access in Low and Middle-Income Countries (LMIC). Tangible outcome measures in existing literature are lacking. An up-scaling and wider adoption of successful strategies is necessary and possible.
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 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 | 0.025 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".