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Record W3021678116 · doi:10.1097/jat.0000000000000140

Early Mobilization of Patients Receiving Vasoactive Drugs in Critical Care Units: A Systematic Review

2020· review· en· W3021678116 on OpenAlexaboutno aff
Prasobh Jacob, Praveen Jayaprabha Surendran, Muhamed Aleef E M, Theodoros Papasavvas, Reshma Praveen, Narasimman Swaminathan, Fiona Milligan

Bibliographic record

VenueJournal of Acute Care Physical Therapy · 2020
Typereview
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsVasoactiveMedicineCochrane LibraryIntensive care medicineMEDLINESystematic reviewCochrane collaborationAlternative medicinePhysical therapyInternal medicinePathology

Abstract

fetched live from OpenAlex

Purpose: Mobilization is feasible, safe, and beneficial to patients admitted to critical care units. Vasoactive therapy appears to be one of the most common barriers to early mobilization. Many recent publications have studied the safety and feasibility of mobilizing patients with these vasoactive drugs. The aim of this review was to synthesize the prevailing evidence pertaining to mobilizing patients receiving vasoactive drugs. Methods: The protocol was developed and registered on PROSPERO (CRD42019127448). A comprehensive literature search was conducted using PubMed, Physiotherapy Evidence Database (PEDRO), Cochrane Central, and Embase (through Cochrane) for original research, including case studies and consensus guidelines. PRISMA guidelines were used to conduct and report this review. The included articles were appraised using the Newcastle-Ottawa Scale independently and a consensus reached by 3 reviewers. Results and Conclusion: Evidence determining specific doses of vasoactive drugs that would allow safe mobilization of patients in critical care is lacking. The criteria that have been used to determine the eligibility to mobilize patients on vasoactive drugs have not been consistent.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.119
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.035
GPT teacher head0.377
Teacher spread0.342 · 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 designSystematic review
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

Citations18
Published2020
Admission routes1
Has abstractyes

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