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Record W3008603125 · doi:10.1186/s13643-020-1281-4

Prognostic-factors for neurodegeneration in chronic moderate-to-severe traumatic brain injury: a systematic review protocol

2020· review· en· W3008603125 on OpenAlexafffund
Bhanu Sharma, Alana Changoor, Leanne Monteiro, Brenda Colella, Robin Green

Bibliographic record

VenueSystematic Reviews · 2020
Typereview
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsUniversity of TorontoMcMaster UniversityToronto Rehabilitation InstituteUniversity Health Network
FundersToronto Rehabilitation InstituteCanada Research Chairs
KeywordsMedicineTraumatic brain injuryNeurodegenerationProtocol (science)Intensive care medicinePsychiatryAlternative medicinePathologyDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Traumatic brain injury (TBI) is a leading cause of death and disability. Recently, a paradigm shift in our understanding of moderate-to-severe TBI has led to its reconceptualization as a progressive neurodegenerative disorder. Widespread progressive atrophy is observed in the months and years post-injury, long after the acute effects of the injury have resolved. Some studies have begun to examine prognostic demographic, injury-related, and post-injury risk factors that contribute to these declines. A synthesis of this information, and in particular, an increased understanding of post-injury factors that may be modifiable, would improve our ability to design interventions to reduce neurodegeneration in moderate-to-severe TBI. This systematic review aims to identify prognostic factors for neural deterioration in moderate-to-severe TBI, and thereby inform future intervention research in this population. METHODS: This review protocol was informed by and conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) guidelines. Search strategies (designed to identify literature on prognostic factors of neurodegeneration in adults with moderate-to-severe TBI) optimized for MEDLINE, EMBASE PsychINFO, CINAHL, SportDiscus, and Cochrane Central Register of Controlled Trials will be developed with the assistance of a health sciences librarian. Retrieved studies will be screened by two team members. Studies must report on longitudinal neuroimaging (i.e., two or more scans in the same cohort) or neuroimaging in a cross-sectional study and potential prognostic factors for neurodegeneration, such as demographics (e.g., gender, age, education), injury (e.g., severity, etiology), or post-injury characteristics (e.g., type and length of therapy, activity level, mood). DISCUSSION: By identifying prognostic factors for neurodegeneration, this systematic review can help inform injury management, as well as intervention research designed to offset the effects of modifiable prognostic factors, such as low levels of cognitive or physical activity. In turn, this systematic review can increase our understanding of how to improve outcome following moderate-to-severe TBI. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019122389.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewlow
gptno category
Domain: not available · Genre: Protocol
About the Canadian research system: no · About a Canadian topic: no
Systematic reviewhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.053
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Meta-epidemiology (broad), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.166
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.053
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0300.003
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.227
GPT teacher head0.463
Teacher spread0.236 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations12
Published2020
Admission routes2
Has abstractyes

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