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Record W1532275465 · doi:10.1186/cc5087

Iatrogenesis, inflammation and organ injury: insights from a murine model.

2006· letter· en· W1532275465 on OpenAlexaff
John C. Marshall

Bibliographic record

VenueCritical Care · 2006
Typeletter
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineInsultCritical illnessIntensive care medicineIntensive care unitOrgan dysfunctionPsychological interventionDiseaseAnimal modelOrgan systemInflammationBioinformaticsCritically illImmunologyPathologySepsisPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

The complex biology of critical illness not only reflects the initial insult that brought the patient to the intensive care unit but also, and perhaps even more importantly, it reflects the consequences of the many clinical interventions initiated to support life during a time of lethal organ system insufficiency. The latter may amplify or modify the response to the former and are eminently amenable to modulation by changes in practice. However, they rarely figure in conceptual models of critical illness and are almost never accounted for in preclinical models of disease. In the preceding issue of Critical Care, O'Mahony and colleagues reported on an animal model in which sequential insults--low-dose endotoxin followed by mechanical ventilation--induce much greater remote organ injury than either insult alone. Although animal models are poor surrogates for clinical illness, studies such as these provide valuable platforms for probing the complex interactions between insult and therapy that give rise to the intricate biology of critical illness.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0130.014
Insufficient payload (model declined to judge)0.0020.002

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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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

Citations3
Published2006
Admission routes1
Has abstractyes

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