Deterioration of spinal sagittal alignment exposes latent cognitive impairment in the general older population: A Japanese cohort survey randomly sampled from a basic resident registry
Bibliographic record
Abstract
Abstract Background: This study investigated the impact of spinal sagittal alignment on cognitive function in the general older population using a Japanese population cohort constructed from random sampling of the basic resident registry of a rural town.Methods: Registered citizens of 50 to 89 years old were targeted for this survey. Participants were classified into 8 groups based on age (50's, 60's, 70's, or 80's) and sex (male or female) after random sampling from the resident registry of a cooperating town in 2014. A total of 413 subjects (203 male and 210 female) were enrolled. We analyzed the distribution of cognitive function test scores determined as by Montreal Cognitive Assessment and Mini-Mental State Examination in each age and sex group to assess the impact of radiographic parameters of spinal sagittal alignment on cognitive function tests.Results: Cognitive function test results tended to decrease with age. Among groups of the same age and sex, cognitive function worsened significantly with poorer spinal alignment. In particular, increases in sagittal vertical axis or global tilt by 1 degree of standard deviation were significantly related to mild cognitive impairment (odds ratio: both 1.4).Conclusions: Spinal alignment deterioration indicated cognitive function decline in Japanese older people of the same age and sex. Thus, a forward shift in sagittal spinal balance may be regarded as a visible indicator of latent mild cognitive impairment in community-dwelling older people.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".