Detection of cognitive decline by spinal posture assessment in health exams of the general older population
Bibliographic record
Abstract
The recent increase in the older adult population has led to a higher prevalence of cognitive impairment, which is often overlooked in routine health examinations. Citizens aged 50-89 years were targeted for this cohort survey by random sampling from the resident registry of a cooperating town in 2014. A total of 411 participants (202 male and 209 female) were enrolled. We analyzed the distribution of cognitive function test scores as determined by Montreal Cognitive Assessment and Mini-Mental State Examination tests in each age (50's, 60's, 70's and 80's) and sex group to examine whether cognitive decline could be detected by sagittal spinal balance measurement based on a radiological approach. Sagittal spinal balance was quantitatively measured as sagittal vertical axis (SVA). We observed significant associations for higher age and/or SVA anteriorization with lower cognitive function. In males, spinal balance anteriorization was associated with cognitive decline independently of age, with combinations of age and SVA also making valid cognitive decline determinations; male cases of SVA ≥ 100 mm at any age, SVA ≥ 90 mm at ≥ 70 years, and SVA ≥ 70 mm at ≥ 80 years were all more likely to have cognitive decline than cases below those values. For females, cognitive decline was more likely in cases of SVA ≥ 70 mm, regardless of age. Thus, spinal balance anteriorization can be regarded as an easily visible indicator of latent cognitive decline 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 | 0.004 |
| 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 | 0.000 | 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".