Cognitive Functions in High-altitude Tribal Population: Assessment Using the Montreal Cognitive Assessment (MoCA) Tool
Bibliographic record
Abstract
Background High altitude (HA) environments expose individuals to chronic hypoxia, which can affect cognitive function. While studies have explored cognitive deficits in lowlanders ascending to HAs, there is limited research on cognitive function among natives of HA. Purpose This study aimed to evaluate cognitive functions in the tribal population of Himachal Pradesh India using the Montreal Cognitive Assessment (MoCA) tool and assess the effect of high and very HAs along with age and gender variation in cognitive functions in these HA natives. Methods This study included 359 participants who lived at HAs. Cognitive function was assessed using MoCA. Participants were stratified by altitude (3,000–3,500 m and >3,500 m) and age group (20–40, 41–60 and >60 years). Appropriate statistical analyses were performed to determine the association of altitude, age, and sex with MoCA total and subscale scores. Results Significant associations were found between altitude and performance on the Naming, Attention, Delayed Recall, and Orientation subscales ( p < .001, p < .001, p < .001 and p = .002, respectively). Specifically, a significant age-related decline was observed across the MoCA total score and most subscales ( p < .001, Kendall’s Tau = 0.48). Gender also had a significant association with the Visuospatial/Executive and Attention subscales ( p < .001 for both), with males scoring higher than females. Conclusion Increasing altitude was associated with lower scores on specific cognitive domains. Age is a primary factor influencing cognitive performance in high-altitude natives and is associated with lower MoCA scores. Gender differences were also observed in specific cognitive functions. These findings suggest that altitude, age, and sex play important roles in shaping the cognitive profiles of individuals living at HAs.
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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 | 0.000 | 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 | 0.000 | 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".