Disturbed social recognition and impaired risk judgement in older residents with mild cognitive impairment after the Great East Japan Earthquake of 2011: the Tome Project
Bibliographic record
Abstract
AIM: After the Great East Japan Earthquake of 2011, we investigated the safety of residents in the affected communities. Most of the people requiring help were elderly and had previously been assessed as Clinical Dementia Rating (CDR) 0.5 (i.e. as having mild cognitive impairment (MCI)). We examined how well they understood the television news and whether they could make appropriate decisions. METHODS: This community-based study of dementia and difficulties following a disaster started in Tome, northern Japan. The subjects were 188 randomly selected older residents who underwent CDR, blood tests, magnetic resonance imaging, and cognitive tests, including an original visual risk cognition task. They were shown NHK news broadcasts from the day of the earthquake to determine whether they could understand the content. RESULTS: Neither the CDR 0 (healthy) nor the CDR 0.5 (MCI) subjects fully understood the television news. Some subjects did not recognize the danger of aftershocks and engaged in risky behaviour. CDR 0.5 subjects who exhibited such behaviour scored lower on the visual risk cognition task. CONCLUSIONS: It is noteworthy that television news is difficult to understand, even for healthy older adults. We found that MCI subjects had particular difficulties due to the disaster and suggest that risk cognition could be evaluated using visually presented materials.
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 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 | 0.001 | 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".