Subjective cognitive complaints in patients with progressive supranuclear palsy
Bibliographic record
Abstract
Introduction: Subjective cognitive complaints (SCC) refer to self-reported cognitive decline that may or may not be reflected in objective neuropsychological evaluations. Such SCC are prevalent in neurodegenerative diseases, including Parkinson's disease (PD), but the prevalence and clinical features in patients with progressive supranuclear palsy (PSP) have not been investigated. Methods: We recruited 83 PSP patients without dementia and investigated their SCC using a semi-structured interview. Comprehensive neuropsychological test results and patient clinical features were compared according to presence of SCC and underlying cognitive state. Results: Among the 83 patients, 16 had normal cognition (NC), 67 had mild cognitive impairment (MCI), and 36 (43.4%) reported SCC. Among NC patients, 37.5% (6/16) had SCC, while 44.8% (30/67) of MCI patients reported SCC. There were no differences between the neuropsychological test results or demographic and clinical characteristics of PSP patients with or without SCC in the NC group. The demographic and clinical characteristics of the MCI+SCC (MCI with SCC)and MCI-SCC (MCI without SCC) groups were comparable, but the MCI+SCC group had significantly worse neuropsychological scores than the MCI-SCC group, particularly in tests assessing attention, language, visual memory, and fronto-executive function domains. Discussion: While SCC are commonly reported by PSP patients, patients with PSP and MCI+SCC had worse cognitive function than those who did not report SCC. These findings suggest that SCC in PSP patients with MCI could be a worsening sign of cognitive function. Therefore, it is crucial for physicians to assess SCC in PSP patients and to provide timely diagnosis and management of cognitive decline.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".