Predictors of Conversion to Dementia in Patients With Mild Cognitive Impairment: The Role of Low Body Temperature
Bibliographic record
Abstract
Background: Subjects with mild cognitive impairment (MCI) can progress to dementia. Studies have shown that neuropsychological tests, biological or radiological markers individually or in combination have helped to determine the risk of conversion from MCI to dementia. These techniques are complex and expensive, and clinical risk factors were not considered in these studies. This study examined demographic, lifestyle and clinical factors including low body temperature that may play a role in the conversion of MCI to dementia in elderly patients. Methods: In this retrospective study, a chart review was conducted on patients aged 61 to 103 years who were seen at the University of Alberta Hospital. Information on onset of MCI and demographic, social, and lifestyle factors, family history of dementia and clinical factors, and current medications at baseline was collected from patient charts on an electronic database. The conversion from MCI to dementia within 5.5 years was also determined. Logistic regression analysis was conducted to identify the baseline factors associated with conversion from MCI to dementia. Results: The prevalence of MCI at baseline was 25.6% (335/1,330). During the 5.5 years follow-up period, 43% (143/335) of the subjects converted to dementia from MCI. The factors that were significantly associated with conversion from MCI to dementia were family history of dementia (odds ratio (OR): 2.78, 95% confidence interval (CI): 1.56 - 4.95, P = 0.001), Montreal cognitive assessment (MoCA) score (OR: 0.91, 95% CI: 0.85 - 0.97, P = 0.01), and low body temperature (below 36 °C) (OR: 10.01, 95% CI: 3.59 - 27.88, P < 0.001). Conclusion: In addition to family history of dementia and MoCA, low body temperature was shown to be associated with the conversion from MCI to dementia. This study would help clinicians to identify patients with MCI who are at highest risk of conversion to dementia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".