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Record W2922517103 · doi:10.1097/jat.0000000000000096

Early Mobilization of Patients With External Ventricular Drains: Does Therapist Experience Matter?

2019· article· en· W2922517103 on OpenAlexaff
Kristen Stout, Nethra Ankam, Mohammad Athar, Paula Bu, Nooreen Dabbish, Benjamin E. Leiby, Sara Melnyk, Syed Omar Shah, Ashley Tarkiainen

Bibliographic record

VenueJournal of Acute Care Physical Therapy · 2019
Typearticle
Languageen
FieldMedicine
TopicIntensive Care Unit Cognitive Disorders
Canadian institutionsBC Research (Canada)
Fundersnot available
KeywordsMedicineMobilizationStaffingIntensive care unitAdverse effectPhysical therapyNursingIntensive care medicineInternal medicine

Abstract

fetched live from OpenAlex

Objective: Growing evidence supports the benefits and safety of early mobilization of patients in intensive care units. Frequently cited barriers to early mobilization are insufficient staffing and training. This study examines the number of professionals and years of physical therapy or occupational therapy experience needed to mobilize patients in neuro-intensive care units with external ventricular drains (EVDs). Design: The study was a retrospective review of a prospective quality improvement database, which includes 185 encounters with 90 patients with EVDs from June 2014 through July 2015. Results: Ninety-five percent of encounters required at most 2 professionals for mobilization. No evidence of association between number of people required to mobilize and highest activity achieved was found. Neither the number of people to mobilize patients nor the primary therapist's years of experience were associated with the type of activity achieved or the occurrence of an adverse event. Conclusion: This analysis suggests that patients with EVDs in the neuro-intensive care unit can be safely and efficiently mobilized by physical therapists and occupational therapists of varying levels of clinical experience. Early mobilization of patients with EVDs may demand fewer staff resources than perceived by clinicians.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.388

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.004
GPT teacher head0.255
Teacher spread0.251 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations1
Published2019
Admission routes1
Has abstractyes

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