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Record W4328050864 · doi:10.1186/s13643-023-02189-2

Sex-based analysis of treatment responses in animal models of sepsis: a preclinical systematic review protocol

2023· article· en· W4328050864 on OpenAlexafffund
MengQi Zhang, Dean Fergusson, Rahul Sharma, Ciel Khoo, Asher A. Mendelson, Braedon McDonald, Kimberly F. Macala, Neha Sharma, Sean E. Gill, Kirsten M. Fiest, Christine Lehmann, Risa Shorr, Forough Jahandideh, Stephane L. Bourque, Patricia C. Liaw, Alison Fox‐Robichaud, Manoj M. Lalu, Marc T. Avey, Emmanuel Charbonney, Arnold S. Kristof, Gloria Vázquez‐Grande, Ruud A. W. Veldhuizen, Brent W. Winston, Salman T. Qureshi, Juan Zhou

Bibliographic record

VenueSystematic Reviews · 2023
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsHamilton Health SciencesThrombosis and Atherosclerosis Research InstituteQueen Elizabeth II Health Sciences CentreLawson Health Research InstituteWestern UniversityMcMaster UniversityUniversity of AlbertaUniversity of CalgaryUniversity of ManitobaOttawa HospitalRoyal Alexandra HospitalUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaOttawa Hospital Anesthesia Alternate Funds AssociationCanadian Institutes of Health ResearchRoyal Alexandra Hospital FoundationCanadian Anesthesiologists' SocietyChildren's Health Research InstituteWomen and Children's Health Research InstituteUniversity of Ottawa
KeywordsMedicineMeta-analysisObservational studyConfoundingProtocol (science)MEDLINEIntervention (counseling)Study heterogeneityData extractionSystematic reviewSepsisResearch designIntensive care medicineAlternative medicineInternal medicinePathologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: The importance of investigating sex- and gender-dependent differences has been recently emphasized by major funding agencies. Notably, the influence of biological sex on clinical outcomes in sepsis is unclear, and observational studies suffer from the effect of confounding factors. The controlled experimental environment afforded by preclinical studies allows for clarification and mechanistic evaluation of sex-dependent differences. We propose a systematic review to assess the impact of biological sex on baseline responses to disease induction as well as treatment responses in animal models of sepsis. Given the lack of guidance surrounding sex-based analyses in preclinical systematic reviews, careful consideration of various factors is needed to understand how best to conduct analyses and communicate findings. METHODS: MEDLINE and Embase will be searched (2011-present) to identify preclinical studies of sepsis in which any intervention was administered and sex-stratified data reported. The primary outcome will be mortality. Secondary outcomes will include organ dysfunction, bacterial load, and IL-6 levels. Study selection will be conducted independently and in duplicate by two reviewers. Data extraction will be conducted by one reviewer and audited by a second independent reviewer. Data extracted from included studies will be pooled, and meta-analysis will be conducted using random effects modeling. Primary analyses will be stratified by animal age and will assess the impact of sex at the following time points: pre-intervention, in response to treatment, and post-intervention. Risk of bias will be assessed using the SYRCLE's risk-of-bias tool. Illustrative examples of potential methods to analyze sex-based differences are provided in this protocol. DISCUSSION: Our systematic review will summarize the current state of knowledge on sex-dependent differences in sepsis. This will identify current knowledge gaps that future studies can address. Finally, this review will provide a framework for sex-based analysis in future preclinical systematic reviews. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022367726.

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 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)0.005
Version: codex-gemma-dda1882f352aValidation 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 score0.115
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0120.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0080.001
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.339
GPT teacher head0.513
Teacher spread0.173 · 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 teacher head, 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

Citations6
Published2023
Admission routes2
Has abstractyes

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