Paramedic assessment of suspected or confirmed COVID-19 patients in the out-of-hospital environment: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to develop a comprehensive collection of information about the current processes for paramedics assessing and referring patients with suspected or confirmed COVID-19 in the out-of-hospital environment. INTRODUCTION: Paramedics and ambulance service clinicians commonly encounter patients with COVID-19. Increased demand on ambulance services has resulted in many of these services developing alternative referral pathways to avoid unnecessary conveyance to emergency departments. There is not a strong body of literature or rigorous clinical practice guideline on this topic to support the assessment and referral decision-making for patients with COVID-19 in the out-of-hospital setting. INCLUSION CRITERIA: Any sources of evidence on patients with suspected or confirmed COVID-19 in the out-of-hospital environment who are seeking care for COVID-19-related symptoms and being assessed by paramedics will be considered for inclusion. Sources from scholarly literature and gray literature, such as ambulance service clinical practice guidelines, will be included. Sources from Australia, Aotearoa New Zealand, the US, Canada, and the UK will be included. METHODS: The review will be guided by the JBI methodology for scoping reviews and will be reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). A database search for scholarly literature will be performed, followed by a gray literature search. Databases will include MEDLINE (Ovid), CINAHL (EBSCOhost), Scopus (Ovid), Emcare (Ovid), Embase (Ovid), amber, JBI Evidence Synthesis , the Cochrane Database of Systematic Reviews, and Epistemonikos. Gray literature will include clinical practice guidelines, protocols, and procedures obtained from ambulance service websites and apps. Results will be presented through figurative, tabular, and narrative synthesis methods. REVIEW REGISTRATION: Open Science Framework https://osf.io/yc7vq.
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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 | 0.082 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.018 | 0.012 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.056 | 0.013 |
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".