March changes as predicting factors of incident depression: a systematic review
Bibliographic record
Abstract
Introduction: Depression is a multifactorial disorder that affects people of all ages and both sexes, associated with emotional and motor disorders, thus impairing the quality of life of these individuals. Objective: To verify whether changes in gait parameters can predict the risk of incident depression. Methodology: The study was characterized as a systematic review, where a search was performed in four databases (PubMed, PsycINFO, Web of Science and SPORTDiscus), including longitudinal cohort articles, with a follow-up of at least one year, written in Portuguese, English or Spanish, which evaluated depression and gait at baseline and the subsequent risk of incident depression according to the alteration of some gait parameter. The risk of bias in the studies was assessed using the NewCastle Ottawa Scale (NOS). Random effects meta-analyses were performed, calculating the risk in adjusted and unadjusted odds ratios (OR) along with the confidence interval. Heterogeneity was evaluated using the I² statistic. Potential sources of heterogeneity (gender, age and follow-up) were explored by meta-regressions. Results: 7 studies were included, where 21,038 participants were evaluated. During the analysis of unadjusted gait speed, the slower people had a 2.88 times greater risk of developing depression, while in the adjusted one, a risk of 1.63. When investigating the speed before and after the potential moderators (gender, age and follow-up), it was noticed that none of the factors evaluated moderated the association between changes in gait and risk of incident depression. The assessment of the quality of the studies achieved a mean score of 7.5 (Standard Deviation = 0.5). Conclusion: A slower walking speed is associated with a higher risk of developing depression.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".