Medical complexity in emergency and urgent care settings: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Considering the impact of non-medical factors (personal and social) on patients with multiple chronic conditions, the term 'medical complexity' is gaining traction as it encompasses both medical and non-medical aspects of patients' medical needs. When primary care is not able to provide timely care for chronic challenges or acute concerns, complex patients require care in emergency or urgent care settings. The concept of medical complexity is continually evolving, although without a universally accepted or standardised definition that determines if an adult patient is considered complex. Therefore, this scoping review aims to understand how medical complexity is defined, identify its defining attributes and examine its use in clinical care research. We also aim to consolidate and evaluate the evidence to suggest a more comprehensive and standardised definition of medical complexity and/or highlight key components required to define medical complexity in urgent care and emergency department settings. METHODS AND ANALYSIS: This protocol is developed according to the approach described by Arksey and O'Malley (2005) and expanded by Levac and colleagues. We will use Walker and Avant's method of concept analysis (2005) to gain a comprehensive understanding of the concept of medical complexity. We will systematically search MEDLINE, CINAHL Plus, EMBASE, APA PsycINFO and Cochrane Library. A grey literature search will be conducted in Google and Google Scholar to identify additional information. Two reviewers will independently screen titles and abstracts for inclusion, followed by a screening of the full text of potentially relevant articles. Relevant data will be extracted from these studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews checklist will be used to report the selection of studies at different stages. ETHICS AND DISSEMINATION: Scoping review methodology uses and reviews publicly available studies and data, so ethics approval is not required. We will disseminate the results of this scoping review through peer-reviewed publications and presentations at academic conferences and scientific meetings. We will also share these results with key stakeholders, including healthcare providers, community organisations and healthcare system leaders.
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 | 0.105 | 0.089 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.015 |
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".