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PRECLINICAL MODELS OF SHOCK AND SEPSIS: WHAT CAN THEY TELL US?

2005· review· en· W1990978595 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueShock · 2005
Typereview
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsIntensive care medicineMedicineSepsisDiseaseTranslational researchClinical trialRelevance (law)Intervention (counseling)Septic shockShock (circulatory)CorollaryNeuroscienceBioinformaticsPsychologyImmunologyPathologyBiologyPsychiatry

Abstract

fetched live from OpenAlex

The goal of translational research is to transform biologic knowledge into new treatments for human disease. Although preclinical models replicate some of the features of the disease process modeled, they invariably fail to reproduce the complexity of human illness, and by their very experimental nature, they are readily manipulated to maximize evidence of efficacy. The result is that successful translation from preclinical models to clinically effective therapy is uncommon, and that clinical trials are often undertaken without a comprehensive and realistic preclinical portfolio of studies to optimize their design. The lethal and morbid human conditions of sepsis and shock are attractive targets for new therapies and enormously challenging processes to translate because they entail considerable clinical heterogeneity, require emergent effective intervention, and are shaped not only by the initial insult, but by approaches to subsequent resuscitation and support. A colloquium jointly sponsored by the Shock Society and the International Sepsis Forum in June 2004 addressed the challenges of translational research in shock and sepsis. Through a comprehensive review of a broad variety of model approaches, and vigorous debate about the merits of differing strategies, a series of common themes emerged. We concluded that there is no single ideal model of shock or sepsis, but rather a large number of complementary models that recapitulate some discrete features of the disorders while minimizing others. Consequently, successful preclinical investigation mandates the use of a panel of preclinical studies consciously designed to address specific questions of relevance to the clinical setting. A corollary of this conclusion is that preclinical studies can shape concepts of disease and can be used to refine decisions regarding optimal patient populations for therapeutic interventional trials. We further recognized that the design and reporting of preclinical studies is highly variable, thereby limiting effective data interpretation and integration between studies. Hence, greater model standardization would aid in interpreting data and in pooling results into systematic data syntheses: such efforts should be promoted and undertaken.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
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 head0.239
GPT teacher head0.434
Teacher spread0.195 · 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