[Relationship of white matter lesions on brain computed tomography and cognitive function in elderly subjects with mild cognitive impairment].
Bibliographic record
Abstract
UNLABELLED: White matter lesions (WML) are frequently disclosed on elderly people computed tomography (CT) brain scan. OBJECTIVE: To evaluate the relationship between WML and cognitive functions of patients suffering from Mild Cognitive Impairment (MCI). METHODS: We studied the association between WML on CT brain scan and cognitive functions in 136 consecutive elderly subjects attending a geriatric outpatient clinic, suffering from MCI. The global cognitive assessment was based on Mini Mental State Examination (MMSE), a validated comprehensive battery of neuropsychological tests, the Cognitive Efficiency Profile (CEP), a CT brain scan and a complete biological screening. WML on CT brain scan was evaluated by a blinded investigator. RESULTS: In this population, 75 +/- 8 years of age, (women 60%, and hypertension 54%), 33% of subjects had WML on CT brain scan. Patients with WML were significantly older (OR=1.27; IC 95%=1.04 - 1.22), had more frequently a past history of hypertension (OR=2.71; IC 95%=1.06 - 6.96) and more frequently lacunae associated with WML (OR=4.48; IC 95%=1.18 - 16.99). Subjects with WML had significantly poorer cognitive functions than those without WML (CEP score/100=62.33 +/- 13.58 versus 71.87 +/- 14.19, p<0.01 and MMSE score/30=27.02 +/- 2.34 versus 27.97 +/- 1.89, p<0.01) CONCLUSION: Our results showed a relationship between WML on CT brain scan and the depth of cognitive dysfunction among MCI patients. Further long term prospective studies have to be performed to determinate if WML are involved in transitions between MCI and Alzheimer' s disease.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".