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Record W2021794570 · doi:10.1002/mds.21283

Gait abnormalities in psychogenic movement disorders

2007· article· en· W2021794570 on OpenAlexaff
Jong Sam Baik, Anthony E. Lang

Bibliographic record

VenueMovement Disorders · 2007
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsToronto Western Hospital
Fundersnot available
KeywordsGaitPsychogenic diseasePhysical medicine and rehabilitationMedicineMovement disordersGait DisturbanceGait analysisPhysical therapyPsychologyInternal medicinePsychiatryDisease

Abstract

fetched live from OpenAlex

An abnormal gait is not uncommon in patients with medically unexplained neurological symptoms, including those with other psychogenic movement disorders (PMDs). Previous studies have not evaluated the gait characteristics of patients with a variety of PMDs and there are no reports comparing PMDs with and without gait disturbances. We were interested in determining how those with and without additional involvement of gait differed and how PMD patients differed from those with a pure psychogenic gait disorder (PGD) in the absence of another PMD. We investigated gait features in a large series of patients with PMD (n = 279), dividing them into two groups (Group I with a normal gait and Group II with an abnormal gait). Group I included those with PMD with a normal gait and no change in the PMD while walking (I-1), and those with a change in PMD while walking, but not affecting gait (I-2). Group II was divided into those with PMD with additional abnormal gait (II-1) and those with pure psychogenic gait disorder without other abnormal movements (II-2). Excessive slowing of movement was more common in PMD patients with an abnormal gait (Group II) compared to those without (Group I). Slowness of gait was the most common feature in patients with PMD combined with a PGD (II-1) and buckling of the knee pattern was the most common type of pure PGD (II-2), followed by astasia-abasia.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.270
Teacher spread0.261 · 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.

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

Citations113
Published2007
Admission routes1
Has abstractyes

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