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Record W2744024249 · doi:10.1136/bmjopen-2017-016194

Optic nerve sheath diameter sonography for the diagnosis of increased intracranial pressure: a systematic review and meta-analysis protocol

2017· review· en· W2744024249 on OpenAlexaff
Alex Koziarz, Niv Sne, Fraser Kegel, Waleed Alhazzani, Siddharth Nath, Jetan H. Badhiwala, Timothy Rice, Paul T. Engels, Samir Faidi, Andrew Healey, Kamyar Kahnamoui, Laura Banfield, Sunjay Sharma, Kesava Reddy, Gregory W. J. Hawryluk, Saleh A. Almenawer

Bibliographic record

VenueBMJ Open · 2017
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury and Neurovascular Disturbances
Canadian institutionsUniversity of CalgaryUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMedicineCochrane LibraryMEDLINECINAHLIntracranial pressureGrading (engineering)Systematic reviewMeta-analysisRandomized controlled trialObservational studyMedical physicsRadiologySurgeryPathologyPsychological intervention

Abstract

fetched live from OpenAlex

INTRODUCTION: Increased intracranial pressure (ICP) is a significant neurological issue that may lead to permanent neurological sequelae. When evaluating patients with traumatic brain injury, it is crucial to identify those with high ICP in order to expedite ICP lowering measures and maintain adequate cerebral perfusion. Several measures are used to recognise patients with increased ICP including CT scan, MRI, ICP monitor, and lumbar puncture (LP). However, these tests can be invasive, associated with radiation exposure, contraindicated, or not readily available. Ultrasonography measurement of the optic nerve sheath diameter (ONSD) is proposed as a non-invasive and quick measure to identify high ICP. The aim of this systematic review and meta-analysis will be to examine the accuracy of ONSD sonography for increased ICP diagnosis. METHODS AND ANALYSES: We will include published and unpublished randomised controlled trials, observational studies, and abstracts, with no publication type or language restrictions. Search strategies will be designed to peruse the MEDLINE, Embase, Web of Science, WHO Clinical Trials, ClinicalTrials.gov, CINAHL, and the Cochrane Library databases. We will also implement strategies to search grey literature. Two reviewers will independently complete data abstraction and conduct quality assessment. Included studies will be assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. We will construct the hierarchical summary receiver operating characteristic curve for included studies and pool sensitivity and specificity using the bivariate model. We also plan to conduct prespecified subgroup analyses to explore heterogeneity. The overall quality of evidence will be rated using Grading of Recommendations, Assessment, Development and Evaluations (GRADE). ETHICS AND DISSEMINATION: Research ethics board approval is not required for this study as it draws from published data and raises no concerns related to patient privacy. This review will provide a comprehensive assessment of the evidence on ONSD sonography diagnostic accuracy and is directed to a wide audience. Results from the review will be disseminated extensively through conferences and submitted to a peer-reviewed journal for publication. PROSPERO REGISTRATION NUMBER: CRD42017055485. CLINICAL TRIAL NUMBER: Trial registration number is NCT00783809.

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 imitation

Not 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.

metaresearch head score (Codex)0.037
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.045
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.072
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0240.024
Bibliometrics0.0130.010
Science and technology studies0.0020.003
Scholarly communication0.0060.006
Open science0.0050.004
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0450.004

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.

Opus teacher head0.331
GPT teacher head0.489
Teacher spread0.158 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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".

Quick stats

Citations26
Published2017
Admission routes1
Has abstractyes

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