ALEXITHYMIA COULD MASK DEPRESSION IN OBESE PATIENTS
Bibliographic record
Abstract
Objective: This study aimed to evaluate the relationship between obesity, alexithymia (primary and secondary) and depression in a sample of obese outpatients. Methods: Among the patients referred to the outpatients’ clinic for obesity in a University Hospital, we consecutively enrolled 100 overweight/obese (BMI > 27 kg/m 2) subjects (35 males and 65 females) over a period of 20 months. Socio-demographic and clinical data were collected; all patients underwent the Toronto Alexithymia Scale (TAS-20) and the Center for Epidemiological Studies Depression Scale (CES-D) in order to measure alexithymia and depression. \nResults: The prevalence of alexithymia was 18% (25% including borderline values). TAS-20 mean score was 49.17 ± 12.38. Considering CES-D scores, 33% of the sample was possibly or probably depressed. CES-D score was significantly correlated to TAS-20 score (r = 0.393, p < 0.001), in particular with DIF (r = 0.524, p < 0.001) and DDF (r = 0.204, p < 0.05) subscales. BMI was not associated with alexithymia nor with depression. \nConclusion: Obesity determines a vulnerability in developing depression, therefore alexithymia in obese depressed patients could be an adaptive response (secondary alexithymia). Moreover alexithymia could lead the subjects to an underestimation of depression and to not seek a correct treatment.From our results, the multidisciplinary approach in treating obese subjects should include the evaluation of emotional aspects whose diagnosis can influence the course of treatment.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".