The prevalence and intensity of late effects in patients with testicular germ cell tumors: A first step of instrument development using a stepwise approach
Bibliographic record
Abstract
PURPOSE: Patients with Testicular Germ Cell Tumors (TGCT) may suffer from several late effects due to their diagnosis or treatment. Follow-up care aims to identify the recurrence of cancer and support patients with TGCT in their experienced late effects. In the Netherlands, the validated Dutch version of the Edmonton Symptom Assessment System, Utrecht Symptom Diary (USD) is used to assess and monitor patient reported symptoms. As a first step to develop a specific USD module for TGCT-patients, it was necessary to identify the prevalence and intensity of late effects in patients with TGCT, covering the physical, social, psychical and existential domains of care. METHODS: A cross-sectional study was conducted. First, literature was systematically assessed to create a comprehensive list of symptoms. This generated list was reviewed by expert healthcare professionals and the research group. Lastly, a survey was distributed amongst patients with TGCT in follow-up care in the University Medical Center Utrecht (UMCU) outpatient clinic. RESULTS: In total, 65 TGCT-patients completed the survey. All described late effects were recognized by TGCT-patients, with 'fatigue', 'disturbed overall well-being', 'concentration problems' and 'neuropathy', indicated as most prevalent and scored with highest intensity. When prioritizing these late effects, patients assigned 'neuropathy' as most important. CONCLUSIONS: This study provided insight into prevalence and intensity of late effects, as indicated by TGCT-patients. In clinical practice, follow-up care can improve by empowering patients to discuss important items in daily life with their health-care professionals.
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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 | 0.018 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".