Alexithymia, Immunity and Cervical Intraepithelial Neoplasia: Replication
Bibliographic record
Abstract
BACKGROUND: In a previous study [Psychother Psychosom 1994;61:199-204] we investigated the relationship between alexithymia, carcinogenesis and immunity in a group of women who were unconscious sufferers from precancerous lesions of the cervix (CIN). The results of this study showed a high level of association between alexithymia and CIN and, an even more interesting fact, between alexithymia and reduced levels of immunity. METHODS: The aim of the present study is to check the results of the previous one by testing a larger group (43 women affected by cervical dysplasia and 67 healthy women) and by the use of a self-administered test for detection of alexithymia, the well-validated Twenty-Item Toronto Alexithymia Scale (TAS-20). RESULTS: The results confirm that women suffering from CIN have higher average TAS-20 ratings (55) than normal women (47.32) and that the level of alexithymia detected in the group of women suffering from dysplasia (42.5%) is higher than that of normal women (12.85%). Moreover, the present study confirms that alexithymic women have lower rates of a number of lymphocyte subsets than non-alexithymic women. CONCLUSIONS: This study fully confirms the results of our previous work and those of a number of other studies: (1) personality might be one of the factors jointly responsible for the outbreak of cancer; (2) the immune system appears to play an important part as a mediator between personality and cancer.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".