Development and psychometric evaluation of a patient-reported symptom index for patients with non-muscle invasive bladder cancer: the NMIBC-SI
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Non-muscle invasive bladder cancer (NMIBC) is a chronic condition requiring frequent follow-up with endoscopic examinations, tumour resections and intravesical treatments. In this clinical context, patient-reported outcomes (PROs) have enormous potential to inform treatment assessment and recommendations for NMIBC. We aimed to develop and evaluate a patient-reported NMIBC Symptom Index (NMIBC-SI) to facilitate clinical research and enhance care. METHODS: NMIBC-SI items were developed based on existing literature and qualitative interviews with patients and clinicians, and evaluated in two field tests: item reduction, using NMIBC-SI data from 220 patients on active treatment from nine Australian centres; reliability and validity evaluation of item-reduced version using NMIBC-SI data from 232 patients from five countries. RESULTS: NMIBC-SI assesses disease and treatment-related symptom burden and two treatment-specific side-effects (cystoscopy, intravesical BCG/Chemotherapy). Composite analysis supported a single composite model including core symptom and cystoscopy index items (Intravesical index items were not tested due to small sample). Test-retest reliability was strong (range 0.894-0.91). As expected, the NMIBC-SI was able to discriminate between no treatment and any treatment groups, and no treatment and chemo/BCG groups, providing evidence towards validity. CONCLUSIONS AND CLINICAL IMPLICATIONS: NMIBC-SI assesses patients' self-reported symptom burden and can be used to evaluate NMIBC treatments from the perspective of patients. The NMIBC-SI is acceptable to patients and has evidence for reliability and validity. Future validation work with patients with greater symptom burden is warranted.
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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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".