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Record W7125232640 · doi:10.1093/jsprm/snaf023

Proportion, etiology, predictors, and clinical outcomes of sepsis in neurosurgical patients: a systematic review and meta-analysis protocol

2025· article· en· W7125232640 on OpenAlexaboutno aff
S Sankar, Josué Aganze Mwambali, Gates Iragi Mulume, Suhani Sharma, Linda J Kelly, Franklin J Crossen, Aura Ilovan, Ihsaan H. Patel, Claire Karekezi

Bibliographic record

VenueJournal of Surgical Protocols and Research Methodologies · 2025
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)SepsisMEDLINESystematic reviewMeta-analysisOdds ratioConfidence intervalReporting biasRisk assessment

Abstract

fetched live from OpenAlex

Abstract Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, with in-hospital mortality rate exceeding 30% in critically ill patients. The increasing burden of sepsis in neurosurgical patients is a complex and poorly understood clinical challenge, underscoring the need for evidence-informed decision-making. This review aims to assess the proportion, etiology, predictors, and clinical outcomes in sepsis in neurosurgical patients. A systematic review and meta-analysis will be conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. Databases to be searched include PubMed, Embase, and Scopus from database inception to date. We will include original peer-reviewed articles reporting primary data. Additionally, reference lists of relevant reviews will be screened for additional eligible studies. Two independent reviewers will screen records, extract data, and assess risk of bias using Newcastle-Ottawa Scale and Cochrane Risk of Bias 2.00 tool. In meta-analysis, the effect will be measured using proportions, odds ratios, relative risks, or mean differences, with their 95% confidence intervals. The I2 and τ2 will describe heterogeneity. Where applicable, meta-regression, subgroup, and sensitivity analyses will be conducted to explore sources of heterogeneity. About 95% prediction intervals will measure the expected range of the effect in future studies. Ethical approval is not required. Findings are expected to inform more effective treatment guidelines, while also guiding public health strategies through optimized resource allocation, increased investment in prophylactic and postoperative care, and evidence-based policy development. Systematic Review Registration: The protocol has been registered on the International Prospective Register of Systematic Review (CRD420251088061).

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)0.075
metaresearch head score (Gemma)0.101
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.080
Threshold uncertainty score0.396

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0750.101
Meta-epidemiology (narrow)0.0070.006
Meta-epidemiology (broad)0.0250.032
Bibliometrics0.0140.011
Science and technology studies0.0030.004
Scholarly communication0.0070.007
Open science0.0060.005
Research integrity0.0080.006
Insufficient payload (model declined to judge)0.0800.009

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 head0.614
GPT teacher head0.639
Teacher spread0.025 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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