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Record W2340895169 · doi:10.1097/mcc.0000000000000298

Flow-directed vs. goal-directed strategy for management of hemodynamics

2016· review· en· W2340895169 on OpenAlexaff
Sheldon Magder

Bibliographic record

VenueCurrent Opinion in Critical Care · 2016
Typereview
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsMedicineIntensive care medicinePerioperativePsychological interventionClinical trialCardiac outputRandomized controlled trialProtocol (science)Critically illIntervention (counseling)HemodynamicsMEDLINECardiologyInternal medicineAlternative medicineSurgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: For the past 15 years, there has been a strong push to use goal-directed protocols for resuscitating critically ill patients and to manage perioperative patients. However, recent large clinical trials have failed to find evidence of improved outcome with this approach. RECENT FINDINGS: A striking feature in the recent three large prospective randomized trials of septic patients and the one in high-risk perioperative patients is that outcomes in the control groups have markedly improved. This implies improvement in care and clinical acumen. Perhaps the clinical approach should be more toward further helping clinicians with their clinical choices. A good example is cardiac output. The objective of most hemodynamic interventions is to increase cardiac output. It would thus make sense to assess what happened to cardiac output after the intervention to determine if the intervention actually increased cardiac output. If it did not, another therapy should be chosen. I call this a flow-directed responsive protocol. SUMMARY: A clinical approach that uses monitored values such as cardiac output as a feedback tool to evaluate the response to therapeutic interventions in individual patients may be better than protocols that set fixed targets for all study participants.

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.832
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
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 head0.175
GPT teacher head0.489
Teacher spread0.314 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations15
Published2016
Admission routes1
Has abstractyes

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