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Nursing Time to Program and Assess Deep Brain Stimulators in Movement Disorder Patients

2005· article· en· W2060707485 on OpenAlex
Karen Hunka, Oksana Suchowersky, Susan M. Wood, Lorelei Derwent, Zelma H. T. Kiss

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 Neuroscience Nursing · 2005
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsDeep brain stimulationDystoniaMedicineMovement disordersEssential tremorWorkloadParkinson's diseasePhysical medicine and rehabilitationPhysical therapyDiseasePsychiatryComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

The use of deep brain stimulation (DBS) to treat movement disorders such as Parkinson's disease, essential tremor, and dystonia is increasing. Although some published literature describes the methods for DBS programming, the time and nursing requirements to run a DBS surgical program have not been examined previously. For this study, we prospectively recorded the time required for both assessments and programming of the DBS from the preoperative period to 1 year after surgery in a variety of patients. Results showed that the mean total time spent programming the stimulator and assessing these patients ranged from 18.0-36.2 hours per patient. It took twice as long to program the stimulator in patients with Parkinson's disease as it did in patients with essential tremor or dystonia. When setting up a program for movement disorders surgery, nursing time spent on patient assessment and programming should be considered in the workload.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.380

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 head0.021
GPT teacher head0.339
Teacher spread0.319 · 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