Mild cognitive impairment: Profile of a cohort from a private sector memory clinic
Bibliographic record
Abstract
BACKGROUND: Private hospital memory clinics might see a different clientele than university or academic institutes due to referral biases. OBJECTIVE: To characterize the profile of patients with mild cognitive impairment (MCI) from a private sector memory clinic. MATERIALS AND METHODS: MCI was diagnosed according to revised clinical criteria of Petersen et al. For a subset of patients with MCI medial temporal atrophy and cerebral small vessel disease (white matter lesions and lacunes) were rated on magnetic resonance imaging (MRI) scans and analyzed for their contribution towards cognitive impairment. RESULTS: Subjects with MCI formed one-third (113/371) of this memory clinic sample from a private hospital. MCI could be effectively diagnosed and subtyped using a brief cognitive scale (Concise Cognitive Test (CONCOG)). The amnestic MCI (single and multiple domains) subtype comprised the majority of cases with MCI. In a subsample of 33 patients, lacunar infarcts were more common than white matter lesions and hippocampal atrophy and were inversely associated with verbal fluency. CONCLUSIONS: MCI may be more commonly encountered in private hospital settings probably due to early referrals. It is possible to diagnose and subtype MCI using a brief cognitive instrument such as the CONCOG. In this sample, lacunar infarcts were more commonly encountered than medial temporal atrophy in such patients.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".