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Record W2003154483

Oropharyngeal Swallowing Disorders in Parkinson?s Disease: revisited

2013· article· en· W2003154483 on OpenAlexaff
Emilia Michou, Laura W. J. Baijens, Laia Rofes, Pilar Sanz Cartgena, Père Clavé

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDysphagia Assessment and Management
Canadian institutionsHealth Sciences Centre
Fundersnot available
KeywordsSwallowingParkinson's diseaseDiseaseMedicinePhysical medicine and rehabilitationQuality of life (healthcare)Position statementNeurophysiologyIntensive care medicinePhysical therapyPsychologyPsychiatryPathologySurgery
DOInot available

Abstract

fetched live from OpenAlex

Abstract: Swallowing impairments in Parkinson’s Disease (PD) affect patients ’ nutritional status, the oral administration of medication, and of course quality-of-life, even in the earlier stages of the disease. Here, we provide a synopsis of the current state of neurological diagnosis and the clinical value of the assessment for nutritional status and swallowing impairments in patients with PD. The recent position statement by European Society for Swallowing Disorders (ESSD) on the clinical assessment of dysphagic PD patients is also reviewed and discussed. Here, we also attempt to summarize and explain the recent findings from neurophysiological studies attempting to underpin the underlying mechanisms of the disease, preceding a short review of the therapeutic approaches. With this review, we aim to increase awareness for the deliberating consequences of swallowing impairments and provide a range of unanswered questions on different levels (physiological, neurophysiological, assessment and therapeutic procedures). Further investigations and collaborative large-scale research studies together with neurophysiological studies seem to be warranted in order to shape more effective clinical practice in the future.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.487
Threshold uncertainty score0.998

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)0.0120.003

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 head0.022
GPT teacher head0.361
Teacher spread0.339 · 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; both teacher heads agree on what is shown here.

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

Citations14
Published2013
Admission routes1
Has abstractyes

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