Bibliographic record
Abstract
PURPOSE OF REVIEW: Previously, outstanding questions have been identified including the relationship of proposed subtypes to etiology, underlying biology, and prognosis. This situation presents an opportunity for major developments in the field. The review summarizes the progress made over the past 1-2 years. RECENT FINDINGS: The etiologic, physiological, and clinical differences between tremor dominant, postural instability gait disorder, and indeterminate phenotypes have been further explored, finding genetic influences, functional imaging and clinical differences. New cluster analyses suggest that nonmotor features are important aspects of Parkinson's disease subtypes, but there was little association found between tremor-dominant /postural instability gait disorder phenotype and nonmotor symptoms. In the cognitive realm, empirically derived subtypes of PD-MCI did not map well onto cognitive subtypes derived using a data-driven approach. In data-driven subtype research, important survival differences between subtypes were identified within the PROPARK database. SUMMARY: It will be important to revisit PD-MCI classification to consider subtyping based upon data that relate cognitive phenotype to prognosis. Given the traction that traditional motor subtyping has had in the field it would be of value to consider how nonmotor symptom clusters can be used with or alongside the motor subtypes. Finally, incorporating subtypes into clinical trials remains a significant gap in Parkinson's disease research.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".