Critical care service delivery across healthcare systems in low-income and low-middle-income countries: protocol for a systematic review
Bibliographic record
Abstract
INTRODUCTION: Critical care in low-income and low-middle income countries (LLMICs) is an underdeveloped component of the healthcare system. Given the increasing growth in demand for critical care services in LLMICs, understanding the current capacity to provide critical care is imperative to inform policy on service expansion. Thus, our aim is to describe the provision of critical care in LLMICs with respect to patients, providers, location of care and services and interventions delivered. METHODS AND ANALYSIS: We will search PubMed/MEDLINE, Web of Science and EMBASE for full-text original research articles available in English describing critical care services that specify the location of service delivery and describe patients and interventions. We will restrict our review to populations from LLMICs (using 2016 World Bank classifications) and published from 1 January 2008 to 1 January 2020. Two-reviewer agreement will be required for both title/abstract and full text review stages, and rate of agreement will be calculated for each stage. We will extract data regarding the location of critical care service delivery, the training of the healthcare professionals providing services, and the illnesses treated according to classification by the WHO Universal Health Coverage Compendium. ETHICS AND DISSEMINATION: Reviewed and exempted by the Stanford University Office for Human Subjects Research and IRB on 20 May 2020. The results of this review will be disseminated through scholarly publication and presentation at regional and international conferences. This review is designed to inform broader WHO, International Federation for Emergency Medicine and partner efforts to strengthen critical care globally. PROSPERO REGISTRATION NUMBER: CRD42019146802.
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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 | 0.096 | 0.110 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.023 | 0.019 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.053 | 0.008 |
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".