Weak Hand Grip Strength Is Associated with Alexithymia in Outpatients in a Mexican Population
Bibliographic record
Abstract
Hand grip strength has been considered as a possible marker for metabolic and psychiatric disease. To date, however, no research has focused on the association between alexithymia and hand grip strength. The objective of the present study was to investigate the correct association between hand grip strength and alexithymia. A cross-sectional study was carried out in Comalcalco, Tabasco, México. A total of 246 individuals were included. Hand grip strength was evaluated in the dominant hand using a Takei® portable digital dynamometer. Alexithymia was measured using the Toronto Alexithymia Scale (TAS-20). Two linear regression models adjusted by confounders were used to determine the association between alexithymia and hand grip strength. The rate for positive alexithymia was 39.0% (n = 94). Individuals with alexithymia showed a weaker hand grip strength than the comparison group (t = 2.4, 244 df, p = 0.01). Individuals with alexithymia had significantly reduced levels of hand grip strength (β = −0.39 ± 0.14; p = 0.006); after additional adjustment for clinical variables, decreased hand grip strength remained (β = 8.00 ± 1.86; p ≤ 0.001). Our results suggest that a decrease in hand grip strength could be associated with alexithymia. This measurement could be useful as a predictive marker for the identification of alexithymia in Mexican individuals who attend outpatient clinics.
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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 | 0.000 | 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".