The impact of telemedicine enabled pre-hospital triage in acute stroke – a protocol for a mixed methods systematic review
Bibliographic record
Abstract
Introduction: Increasing access to thrombolysis and thrombectomy through improved pathway organisation remains a health service challenge that requires contextualisation to the geographic, demographic and resourcing status of any regional stroke service. Pre-hospital delays or delays during inter-hospital transfers can result in patients being outside the window for one or both interventions. Pre-hospital triage using technology-enabled interdisciplinary communication networks may facilitate rapid individualized care decisions, permitting streamlined care pathways to hospital sites most appropriate to their clinical presentation and history in the first instance. Understanding the experience of those involved in efforts to improve or reorganise care may help to explain the impact observed. Objectives: 1. To review the impact of pre-hospital telemedicine enabled workflow intervention strategies on patient outcomes and on service process metrics in hyper-acute stroke care2. To examine how the experience of those involved in providing or receiving such interventions might identify key characteristics of effective interventions. Inclusion criteria: Quantitative, qualitative and primary mixed methods studies will be included. Quantitative studies will assess effectiveness of telemedicine-enabled interventions that facilitate pre-hospital acute stroke triage. Intervention effects on functional outcomes of patients, on intervention rates and on key time metrics in hyperacute stroke care will be assessed. Qualitative studies will explore the experiences of people involved in or impacted by these interventions. Methods and analysis: A convergent segregated mixed methods systematic review will synthesise and integrate primary qualitative, quantitative and mixed methods studies using the Joanna Briggs Institute methodology. Database searches will include OVID (MEDLINE), EMBASE, The Cochrane Library, CINAHL and Web of Science. Critical appraisal will include the Mixed Methods Assessment Tool. Results of quantitative studies and findings of qualitative studies will be integrated and configured to explore and contextualize each single method synthesis. Systematic review registration: This protocol has been submitted for registration with PROSPERO.
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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 | 0.095 | 0.116 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.048 | 0.007 |
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".