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Record W2385820965 · doi:10.1016/j.pmrj.2016.04.005

Implementation of Actigraphy in Acute Traumatic Brain Injury (TBI) Neurorehabilitation Admissions: A Veterans Administration TBI Model Systems Feasibility Study

2016· article· en· W2385820965 on OpenAlexaff
Stephanie J. Towns, Jamie M. Zeitzer, Joel E. Kamper, Erin Holcomb, Marc A. Silva, Daniel J. Schwartz, Risa Nakase‐Richardson

Bibliographic record

VenuePM&R · 2016
Typearticle
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsGeneral Dynamics (Canada)
FundersNational Institute on Disability, Independent Living, and Rehabilitation ResearchDefense and Veterans Brain Injury CenterU.S. Department of Veterans Affairs
KeywordsMedicineNeurorehabilitationTraumatic brain injuryActigraphyRehabilitationVeterans AffairsPhysical therapyPopulationMedical recordRetrospective cohort studyPolytraumaPhysical medicine and rehabilitationEmergency medicinePsychiatrySurgeryInternal medicineCircadian rhythm

Abstract

fetched live from OpenAlex

BACKGROUND: Sleep problems and disorders are prevalent in patients with traumatic brain injury (TBI) and are associated with negative outcomes. Incidence varies because of challenges including differences in assessment methods, particularly in the acute stages of recovery when patients are cognitively impaired and unable to complete traditional self-report methods. Actigraphy (ACG) recently has been validated in the acute TBI rehabilitation setting and may serve as a superior method of assessing sleep-wake patterns at this stage of recovery. Although a few studies with small sample sizes have described the use of ACG, none have described feasibility and implementation protocols. OBJECTIVE: To describe the feasibility and implementation protocol of ACG to evaluate sleep-wake patterns and white-light exposure data in patients with acute TBI during inpatient rehabilitation. Sleep-wake patterns and light exposure data are presented to characterize the sample using these methods to inform future research. DESIGN: Retrospective study. SETTING: Acute inpatient rehabilitation unit at a Veterans' Affairs Polytrauma Rehabilitation Center. PARTICIPANTS: Veterans (age ≥18 years) admitted to inpatient rehabilitation and enrolled in the Traumatic Brain Injury Model Systems study who were admitted and discharged in the calendar year 2013. METHODS: Veterans underwent actigraph watch placement as soon as possible after admission. Records from the calendar year 2013 were reviewed to determine the number of admissions that met study criteria and what percentage of those patients had 3 days of continuous ACG data collected. The barriers to successful watch placement in this population were reviewed. Average sleep, light, and wake data from available records were collected for the study sample. MAIN OUTCOME MEASUREMENTS: Percentage of patients who met study criteria and who had 72 hours of continuous ACG data collected. The barriers to successful watch placement in this population were reviewed. Average sleep, light, and wake data from available records were collected. RESULTS: Of 22 eligible Traumatic Brain Injury Model Systems admissions, 3 consecutive nights of ACG data were successfully obtained for 86% (n = 19) of the sample. Barriers to data collection included patient access due to abbreviated lengths of stay, staff availability for ACG placement, and data collection protocols to prevent loss of data in Veterans' Affairs computing systems. CONCLUSIONS: ACG is feasible for collecting data about sleep, wake, and light exposure in patients who are in acute TBI inpatient rehabilitation settings. LEVEL OF EVIDENCE: III.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.293
Threshold uncertainty score0.613

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.138
GPT teacher head0.460
Teacher spread0.322 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

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