Bibliographic record
Abstract
Objective To study the value of Cog-12 in predicting cognitive impairment in the elderly.Methods At the beginning,56 individuals were enrolled into this study including mild cognitive impairment(MCI)patients and normal cognitive function individuals-involving a control group(n=18),11 patients with normal cognitive function who converted to MCI patients served as a CM group,10 MCI patients with their MCI maintaining stable during the 2-years follow-up period served as MM group,11 MCI patients who converted to AD patients served as an AD group.The patients were scored according to Cog-12,MoCA and MMSE,and their cognitive function was assessed at admission and after two years of follow-up.Results The MoCA and MMSE scores were significantly higher whereas the Cog-12-Ⅰscore was significantly lower in control group than in CM group(P0.05).The MoCA score was significantly higher whereas the total Cog-12-Ⅰand Cog-12 scores were significantly lower in MM group than in AD group(P0.05).The Cog-12 score was a predicting factor for MCI in patients with normal cognitive function(P=0.029).The area under ROC curve showed that the sensitivity of Cog-12 score in predicting MCI was 81.4% when its cutoff value was 4.5.Conclusion Cog-12 is an effective tool for assessing the intelligence,mental state and behaviors of cognitive impairment patients and can thus predict their cognitive impairment.
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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) | 0.001 | 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 | 0.000 | 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".