Post-operative course after papilloma resection: effects of written disclosure of the experience in subject with different alexithymia levels
Bibliographic record
Abstract
Objective: The aim of the investigation was to assess the effects on post-operative course after bladder papilloma resection of a technique for the written disclosure of traumatic events, in interaction with individual differences in alexithymia. Method: 40 Ss. were administered a general questionnaire and the Toronto Alexithymia Scale (TAS-20) the second day after admittance. 20 Ss. were asked to write for 3 days, 20 minutes a day, about their experience of being in hospital, following instructions developed by J.W. Pennebaker and co-workers. The post-operative course was assessed objectively by the duration of stay in hospital and subjectively by Ss. completing the Symptom Check List 90 (SCL 90) the day before leaving hospital. Results: Ss. who wrote stayed fewer days in hospital and showed lower SCL 90 scores. The same effect was shown by low alexithymia levels. Study of interactions showed that the effect of writing was apparent only in Ss. high in alexithymia, while Ss. low in alexithymia showed a favourable course independent of writing. Conclusions: Writing about one's thoughts and feelings about being in hospital for a surgical operation shows beneficial effects on post-operative course. This holds particularly true for high alexithymic Ss., who obtain through writing the same outcome as low alexithymic Ss.
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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".