Postoperative Course After Papilloma Resection: Effects of Written Disclosure of the Experience in Subjects With Different Alexithymia Levels
Bibliographic record
Abstract
OBJECTIVE: The aim of the investigation was to assess the effects on postoperative course after bladder papilloma resection of a technique for the written disclosure of traumatic events in interaction with individual differences in alexithymia. METHODS: Forty subjects were administered a general questionnaire and the Toronto Alexithymia Scale (TAS-20) the second day after admittance. Twenty subjects were asked to write for 3 days, 20 minutes a day, about their experience of being in the hospital, following instructions developed by J. W. Pennebaker and coworkers. The postoperative course was assessed objectively by the duration of stay in hospital and subjectively by subjects completing the Symptom Check List 90 (SCL-90) the day before leaving the hospital. RESULTS: Subjects who wrote stayed fewer days in hospital and had 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 subjects high in alexithymia, whereas subjects low in alexithymia showed a favorable course independent of writing. CONCLUSIONS: Writing about one's thoughts and feelings about being in hospital for a surgical operation has beneficial effects on postoperative course. This holds particularly true for high alexithymic subjects, who obtain through writing the same outcome as low alexithymic subjects.
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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) | 0.001 | 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".