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Record W2788066226 · doi:10.1186/s13054-018-1942-z

The apelinergic system as an alternative to catecholamines in low-output septic shock

2018· article· en· W2788066226 on OpenAlexafffund
David Coquerel, Xavier Sainsily, L Dumont, Philippe Sarret, Éric Marsault, Mannix Auger‐Messier, Olivier Lesur

Bibliographic record

VenueCritical Care · 2018
Typearticle
Languageen
FieldMedicine
TopicApelin-related biomedical research
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaMitacsInstitute of Circulatory and Respiratory HealthCanadian Institutes of Health ResearchCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsSeptic shockMedicineIntensive care medicineResuscitationSAFERShock (circulatory)HomeostasisBlood pressureSepsisAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Catecholamines, in concert with fluid resuscitation, have long been recommended in the management of septic shock. However, not all patients respond positively and controversy surrounding the efficacy-to-safety profile of catecholamines has emerged, trending toward decatecholaminization. Contextually, it is time to re-examine the "maintaining blood pressure" paradigm by identifying safer and life-saving alternatives. We put in perspective the emerging and growing knowledge on a promising alternative avenue: the apelinergic system. This target exhibits invaluable pleiotropic properties, including inodilator activity, cardio-renal protection, and control of fluid homeostasis. Taken together, its effects are expected to be greatly beneficial for patients in septic shock.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.398
Teacher spread0.366 · 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

Citations28
Published2018
Admission routes2
Has abstractyes

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