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Record W2913935205 · doi:10.4103/aian.aian_357_18

Functional outcome of bilateral subthalamic nucleus-deep brain stimulation in advanced parkinson's disease patients: A prospective study

2019· article· en· W2913935205 on OpenAlexaboutno aff
Swetha Tandra, Balakrishna Ramavath, Shaik Afshan Jabeen, M. Kannan, Rupam Borgohain

Bibliographic record

VenueAnnals of Indian Academy of Neurology · 2019
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDeep brain stimulationSubthalamic nucleusParkinson's diseaseRating scaleQuality of life (healthcare)MedicineMontreal Cognitive AssessmentPhysical therapyAdverse effectVerbal fluency testProspective cohort studyPsychologyDiseaseInternal medicineCognitionNeuropsychologyCognitive impairmentPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Deep brain stimulation (DBS) is an accepted modality of treatment in patients with Parkinson's disease (PD). Although DBS was approved in advanced PD, it is being done in early PD as well. It was mainly developed to help the patients of PD to overcome the adverse motor effects associated with treatment and treatment failure. OBJECTIVE: The objective is to study the efficacy of subthalamic nucleus (STN)-DBS procedure in patients with PD. MATERIALS AND METHODS: -test. RESULTS: = 0.1466). Similar findings were also observed for MOCA subscores, but there was significant improvement of verbal fluency in all patients. Quality of life(QoL) improved significantly in all patients after STN-DBS intervention in all areas. Lower baseline UPDRS-III scores were found to enhance the QoL both in "off" and "on" state. However, prolonged disease duration and older age at PD onset were found to be hampering factors in the improvement of QoL. CONCLUSIONS: STN-DBS is a safe procedure and can be performed in all patients of PD who develop disabling motor fluctuations to improve their QoL irrespective early or advanced disease.

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 score0.004
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.036
GPT teacher head0.324
Teacher spread0.288 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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