Effect of forgetting curve based self-management on cognitive function， daily living ability and treatment efficacy of patients with mild cognitive impairment
Bibliographic record
Abstract
ObjectiveTo discuss the effect of forgetting curve based self-management on cognitive function， daily living ability and treatment efficacy of patients with mild cognitive impairment （MCI）.MethodsSimple random sampling method was adopted to enroll 162 MCI patients who met the diagnostic criteria of "Expert Consensus on the Prevention and Treatment of Cognitive Impairment in China" in Nanchong Physical and Mental Hospital and Gaoping Ledele Seniors-Oriented Apartment from April 2020 to June 2021. The selected individuals were classified into study group and control group according to random number table methods， each with 81 cases. Both groups received routine intervention， based on this， study group received the forgetting curve based self-management. The interventions lasted for 3 months in two groups， and patients were assessed using Montreal Cognitive Assessment Scale （MoCA） and Activity of Daily Living Scale （ADL） at the baseline and end of interventions. Then the clinical efficacy was compared between groups.ResultsAfter intervention， an increase was found in MoCA and ADL scores in both groups （tcontrol group=25.004， 12.503， tstudy group=48.211， 24.949， P<0.01）， and post-intervention MoCA and ADL scores in study group were higher than those in control group （t=28.527， 9.433， P<0.01）. The overall efficacy rate was 86.42% in control group， which was lower than 96.30% in study group， with statistical difference （χ²=5.004， P<0.05）.ConclusionForgetting curve based self-management may ameliorate the cognitive function and daily living ability in MCI patients， thus improving the treatment efficacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".