Digital Innovation in Oncological Primary Treatment for Well-Being of Patients: Psychological Caring as Prompt for Enhancing Quality of Life
Bibliographic record
Abstract
One side-effect of oncological treatment is chemotherapy-induced alopecia (CIA), a temporary form of hair loss that could influence patients' mental health. Digitised scalp cooling systems are assuming an important role in the clinical setting during adjuvant treatment, promising hair loss prevention and allowing an efficient procedure to reinforce patients' mental health during chemotherapy by avoiding CIA. The present study was carried out through two research protocols: in Research Protocol 1, we conducted a randomised clinical study to evaluate the emotional impact of using scalp cooling technology in women with BC compared with a traditional chemotherapy setting; in Research Protocol 2, we conducted an observational pre-post study involving women with BC diagnosis being under adjuvant chemotherapy in two experimental conditions: no scalp cooling application and scalp cooling application. Seventy-four women undergoing chemotherapy, aged 30-55 years, were enrolled in both research protocols. We investigated oncological patients' psychological dimensions including body image, body appreciation, expectations, and satisfaction with the scalp cooling treatment, with reference to chemotherapy treatment applying the scalp cooling solution. Our data evidenced the need to implement a supportive clinical approach via brief, tailored psychological intervention addressing patients' progressive adaptation to chemotherapy adverse events and their concerns regarding induced alopecia and the value of the scalp cooling system. Patients receiving the innovative chemotherapy probably coped with it by neglecting its physical impact, instead focusing on avoiding alopecia by using the technological solution and neglecting the emotional impact of chemotherapy as a severe pharmacological 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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".