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Record W3083333312 · doi:10.1136/bmjopen-2020-037014

Immunonutrition for traumatic brain injury in children and adolescents: protocol for a systematic review and meta-analysis

2020· review· en· W3083333312 on OpenAlexaboutno aff
Rong Peng, Hailong Li, Lijun Yang, Xinwei Chen, Linan Zeng, Zhenyan Bo, Lingli Zhang

Bibliographic record

VenueBMJ Open · 2020
Typereview
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsnot available
FundersNational Major Science and Technology Projects of ChinaEducation Department of Sichuan ProvinceChinese Pharmaceutical Association
KeywordsMedicineTraumatic brain injuryCochrane LibraryPerioperativeMEDLINEClinical trialIntensive care medicineAdverse effectRandomized controlled trialObservational studyMeta-analysisSystematic reviewPediatricsInternal medicineSurgeryPsychiatry

Abstract

fetched live from OpenAlex

INTRODUCTION: Traumatic brain injury (TBI) is the leading cause of paediatric trauma death and disability worldwide. The 'Guidelines for the Management of Severe Traumatic Brain Injury (Fourth Edition)' recommend that nutritional goals should be achieved within 5-7 days of injury. Immune-enhancing nutrition or immunonutrition, referring to the addition of specialised nutrients, including glutamine, alanine, omega-3 fatty acids and nucleotides, to standard nutrition formulas, may improve surgical outcomes in the perioperative period. However, the role of immune-enhancing nutritional supplements for patients with paediatric TBI remains unclear. We will conduct a systematic review to determine the efficacy and safety of immunonutrition for patients with paediatric TBI and provide evidence for clinical decision-making. METHODS AND ANALYSIS: Studies reporting immune-enhancing nutrition treatments for patients with paediatric TBI will be included. Outcomes of interest include the length of hospital stay, wound infections, all-cause mortality, non-wound infection, including pneumonia, urinary tract infection and bacteraemia, and the reports adverse events. Duration of follow-up has no restriction. Primary studies consisting of randomised controlled trials (RCTs) and non-RCTs will be eligible for this review, and only studies published in English will be included. We will search the Medline, Embase and Cochrane Library databases from their inception dates to January 2020. We will also search clinicaltrials.gov and the WHO International Clinical Trials Registry Platform for additional information. Two reviewers will independently select studies and extract data. Risk-of-bias will be assessed with tools based on the Cochrane risk-of-bias criteria and Newcastle-Ottawa Quality Assessment Scale. A meta-analysis will be used to pool data when there are sufficient studies with homogeneity. Heterogeneity of the estimates across studies will be assessed; if necessary, a subgroup analysis will be performed to explore the source of heterogeneity. The Grades of Recommendation, Assessment, Development and Evaluation method will be applied to assess the level of evidence obtained from this systematic review. ETHICS AND DISSEMINATION: The proposed systematic review and meta-analysis will be based on published data, and thus ethical approval is not required. The results of this review will be published. PROSPERO REGISTRATION NUMBER: CRD42020154814.

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)0.046
metaresearch head score (Gemma)0.065
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 score0.061
Threshold uncertainty score0.242

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0460.065
Meta-epidemiology (narrow)0.0050.004
Meta-epidemiology (broad)0.0220.027
Bibliometrics0.0090.010
Science and technology studies0.0020.002
Scholarly communication0.0060.005
Open science0.0050.004
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0610.004

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 head0.244
GPT teacher head0.528
Teacher spread0.283 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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