Alexithymia in oncologic disease: association with cancer invasion and hemoglobin levels.
Bibliographic record
Abstract
BACKGROUND: The literature suggests that alexithymia is the result of individual differences and/or biological mechanisms. Both individual differences and disease mechanisms may play a role among individuals with medical or surgical conditions. The relative weight of clinical and individual differences factors related to alexithymia has not been studied in patients with cancer. The purpose of this study was to examine the extent to which individual differences in perceived stress and biological markers of illness severity are associated with alexithymia among patients with cancer. METHODS: Treated oncologic outpatients (N = 37) were assessed using the 20-item Toronto Alexithymia Scale and Perceived Stress Scale. Alexithymia was examined in relation to perceived stress, tumor staging, and hemoglobin levels. RESULTS: Among the patients studied, 34.2% endorsed the established cutoff score (≥61) for alexithymia. Higher alexithymia scores were found in patients with more advanced stages of cancer invasion. Alexithymia correlated directly with perceived stress and indirectly with hemoglobin levels. Hemoglobin levels and cancer invasion significantly correlated with alexithymia when controlling for perceived stress. CONCLUSIONS: A significant component of alexithymia in cancer patients may be construed as acquired. But awareness of health status influencing perceived stress might partially mediate the role of cancer invasion and hemoglobin on alexithymia.
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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 | 0.002 |
| 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) | 0.002 | 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".