Predictors and Pathophysiology of Axial Postural Abnormalities in Parkinsonism: A Scoping Review
Bibliographic record
Abstract
Background: Postural abnormalities involving the trunk are referred to as axial postural abnormalities and can be observed in over 20% of patients with Parkinson's disease (PD) and in atypical parkinsonism. These symptoms are highly disabling and frequently associated with back pain and a worse quality of life in PD. Despite their frequency, little is known about the pathophysiology of these symptoms and scant data are reported about their clinical predictors, making it difficult to prompt prevention strategies. Objectives: We conducted a scoping literature review of clinical predictors and pathophysiology of axial postural abnormalities in patients with parkinsonism to identify key concepts, theories and evidence on this topic. Methods: We applied a systematic approach to identify studies, appraise quality of evidence, summarize main findings, and highlight knowledge gaps. Results: Ninety-two articles were reviewed: 25% reported on clinical predictors and 75% on pathophysiology. Most studies identified advanced disease stage and greater motor symptoms severity as independent clinical predictors in both PD and multiple system atrophy. Discrepant pathophysiology data suggested different potential central and peripheral pathogenic mechanisms. Conclusions: The recognition of clinical predictors and pathophysiology of axial postural abnormalities in parkinsonism is far from being elucidated due to literature bias, encompassing different inclusion criteria and measurement tools and heterogeneity of patient samples. Most studies identified advanced disease stage and higher burden of motor symptoms as possible clinical predictors. Pathophysiology data point toward many different (possibly non-mutually exclusive) mechanisms, including dystonia, rigidity, proprioceptive and vestibular impairment, and higher cognitive deficits.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".