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Record W4320898898 · doi:10.1016/j.brs.2023.01.590

Predictors of future deep brain stimulation surgery in de novo Parkinson’s disease: analysis of the PPMI cohort

2023· article· en· W4320898898 on OpenAlexaff
Stefan Lang, Christopher R. Conner, Artur Vetkas, Andrés M. Lozano, Suneil K. Kalia

Bibliographic record

VenueBrain stimulation · 2023
Typearticle
Languageen
FieldMedicine
TopicNeurological disorders and treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDeep brain stimulationParkinson's diseaseCohortMedicineNeuroscienceDiseasePsychologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction: Deep brain stimulation (DBS) surgery is offered to a subset of Parkinson’s disease (PD) patients. It is unclear if there are features at diagnosis that predict future DBS surgery. Our primary aim was to assess predictors of eventual DBS surgery in de novo PD patients. A secondary aim was to characterize disease progression over four years. Methods: Subjects from the Parkinson’s Progression Marker Initiative (PPMI) database with newly diagnosed, sporadic PD (n=416) were identified and their eventual DBS status was noted (DBS+, n=43; DBS-, n=373). Fifty baseline clinical, imaging, and biospecimen features were extracted for each subject and cross-validated lasso regression was used for feature reduction. The identified features were used in multivariate logistic regression to assess their relationship with DBS status and a receiver operating characteristic curve was constructed to evaluate model performance. Linear mixed effect models were used to explore differences in disease progression over four years. Results: Age at symptom onset, Hoehn and Yahr (H&Y) stage, tremor score, and ratio of CSF Tau to amyloid-beta 1-42 (Tau:Ab) were identified as important baseline features for predicting DBS surgery. A multivariate logistic regression demonstrated age (p < .001), H&Y stage (p = 0.026), tremor score (p <.001), and CSF Tau:Ab (p = .003) independently predicted DBS surgery (area under the curve = 0.83). DBS- patients had faster memory decline (p < .05), while DBS+ patients had faster decline in H&Y stage (p < .001) and motor scores (p<0.05).View Large Image Figure ViewerDownload Hi-res image Download (PPT)View Large Image Figure ViewerDownload Hi-res image Download (PPT) Conclusion: The identified features can be used for early identification of patients who may be surgical candidates during the course of their disease. Disease progression in these groups reflects surgical eligibility criteria, with DBS- patients having more rapid decline in memory while DBS+ patients experienced a faster decline in motor scores. Research Category and Technology and Methods Clinical Research: 17. Epidemiology Keywords: Deep brain stimulation, Parkinson's disease, Predictors

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)0.001
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 score0.016
Threshold uncertainty score0.336

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.017
GPT teacher head0.279
Teacher spread0.262 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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