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Record W3115984244 · doi:10.1177/0885066620982585

Macrocirculatory and Microcirculatory Endpoints in Sepsis Resuscitation

2020· article· en· W3115984244 on OpenAlex
Garrick Mok, Ariel Hendin, Peter M. Reardon, Michael Hickey, Sara Gray, Krishan Yadav

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

VenueJournal of Intensive Care Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSt. Michael's HospitalUniversity of TorontoSt Joseph's Health CentreOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineResuscitationSepsisSeptic shockPerfusionMicrocirculationCapillary refillIntensive care medicineShock (circulatory)Mean arterial pressureCardiologyBlood pressureEmergency medicineInternal medicineHeart rate

Abstract

fetched live from OpenAlex

Sepsis is a common disease process encountered by physicians. Sepsis can lead to septic shock, which carries a hospital mortality rate in excess of 40%. Although the Surviving Sepsis Guidelines recommend targeting a mean arterial pressure (MAP) of 65 mmHg and normalization of lactate, these endpoints do not necessarily result in tissue perfusion in states of shock. While MAP and lactate are commonly used markers in resuscitation, clinicians may be able to improve their resuscitation by broadening their assessment of the microcirculation, which more adequately reflects tissue perfusion. As such, in order to achieve a successful resuscitation, clinicians must optimize both macrocirculatory (MAP, cardiac output) and microcirculatory (proportion of perfused vessels, lactate, mottling, capillary refill time) endpoints. This review will summarize various macrocirculatory and microcirculatory markers of perfusion that can be used to guide the initial resuscitation of patients with sepsis.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.075
GPT teacher head0.342
Teacher spread0.267 · 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