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Record W2297673111 · doi:10.1097/aln.0000000000001083

Neural Control of Inflammation

2016· article· en· W2297673111 on OpenAlexaff
Benjamin E. Steinberg, Eva Sundman, Niccolò Terrando, Peder S. Olofsson

Bibliographic record

VenueAnesthesiology · 2016
Typearticle
Languageen
FieldNeuroscience
TopicVagus Nerve Stimulation Research
Canadian institutionsUniversity of Toronto
FundersNational Heart, Lung, and Blood Institute
KeywordsMedicineInflammationReflexSepsisIntensive care medicinePerioperativeContext (archaeology)IschemiaSystemic inflammationNeuroscienceAnesthesiaImmunologyInternal medicine

Abstract

fetched live from OpenAlex

AbstractAbstract Inflammation and immunity are regulated by neural reflexes. Recent basic science research has demonstrated that a neural reflex, termed the inflammatory reflex, modulates systemic and regional inflammation in a multiplicity of clinical conditions encountered in perioperative medicine and critical care. In this review, the authors describe the anatomic and physiologic basis of the inflammatory reflex and review the evidence implicating this pathway in the modulation of sepsis, ventilator-induced lung injury, postoperative cognitive dysfunction, myocardial ischemia–reperfusion injury, and traumatic hemorrhage. The authors conclude with a discussion of how these new insights might spawn novel therapeutic strategies for the treatment of inflammatory diseases in the context of perioperative and critical care medicine. Neural reflexes modulate systemic inflammation in clinical conditions encountered in perioperative and critical care. This review discusses how recent studies in this area are leading to new therapeutic strategies for the treatment of inflammatory diseases.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.044
GPT teacher head0.306
Teacher spread0.262 · 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 designTheoretical or conceptual
Domainnot available
GenreReview

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

Citations59
Published2016
Admission routes1
Has abstractyes

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