Paratonia in Dementia: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Paratonia is a dementia-induced motor abnormality. Although paratonia affects virtually all people with dementia, it is not well known among clinicians and researchers. OBJECTIVE: The aim of this study was to perform a systematic review of the literature on the definition, pathogenesis, diagnosis, and intervention of paratonia as well as to propose a research agenda for paratonia. METHODS: In this systematic review, the Embase, PubMed, CINAHL, and Cochrane CENTRAL databases were searched for articles published prior to December 2019. Two independent reviewers performed data extraction and assessed the risk of bias of the studies. The following data were extracted: first author, year of publication, study design, study population, diagnosis, assessment, pathogenesis, therapy and interventions. RESULTS: Thirty-five studies met the inclusion criteria and were included. Most studies included in the review mention clinical criteria for paratonia. Additionally, pathogenesis, method of assessment, diagnosis, and paratonia severity as are interventions to address paratonia are also discussed. CONCLUSION: This systematic review outlines what is currently known about paratonia, as well as discusses the preliminary research on the underlying mechanisms of paratonia. Although paratonia has obvious devastating impacts on health and quality of life, the amount of research to date has been limited. In the last decade, there appears to have been increased research on paratonia, which hopefully will increase the momentum to further advance the field.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".