Diagnosis and Management of Traumatic Subarachnoid Hemorrhage: Protocol for a Scoping Review
Bibliographic record
Abstract
BACKGROUND: Globally, 69 million people suffer from traumatic brain injury (TBI) each year and TBI is the most common cause of subarachnoid hemorrhage (SAH). Traumatic SAH (TSAH) has been described as an adverse prognostic factor leading to progressive neurological deterioration and an increase in morbidity and mortality, but there are a limited number of studies which evaluate recent trends in the diagnostic and management of SAH in the context of trauma. OBJECTIVE: The objective of this scoping review was to understand the extent and type of evidence in relation to the diagnostic criteria and management of TSAH. METHODS: This scoping review will be conducted in accordance with the Joanna Briggs Institute methodology for scoping reviews. A 3-step search strategy (an initial limited search in PubMed and Scopus databases; a main search of EMBASE, Web of Science, EBSCO, MEDLINE; and manual searches of reference lists of included articles) will be utilized. The search will be limited to studies with human participants and published in English, Spanish, and French between 2005 and 2020. This review will consider studies of adolescent and adult patients with SAH secondary to trauma. Study selection will be performed by 2 authors (DG and LF) in a 2-phase process; if any disagreement arises, a third author (AR) will be consulted. Data to be extracted from each study will include population, intervention, comparator and outcome measures, and a summary of findings. Citation screening, full-text review, risk of bias assessment, and extraction of study characteristics and outcomes will be carried out using a web-based software platform that streamlines the production of scoping reviews. RESULTS: Ethics approval is not required for this systematic review, as there will be no patient involvement. The search for this systematic review commenced in December 2020, and we expect to publish the findings in early 2021. The plan for dissemination is to publish review findings in a peer-reviewed journal and present findings at conferences that engage the most pertinent stakeholders. CONCLUSIONS: This scoping review will serve as an initial step in providing more evidence for health care professionals, economists, and policymakers so that they might devote more resources toward this significant problem affecting both health and economic outcomes worldwide. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/26709.
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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 | 0.065 | 0.060 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.015 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.071 | 0.012 |
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".