Re‐validation and screening capacity of the 6‐item version of the Cancer Worry Scale
Bibliographic record
Abstract
OBJECTIVE: Fear of cancer recurrence (FCR) is one of the major existential unmet needs of cancer survivors. Due to growing availability of evidenced-based interventions for high FCR, valid and reliable brief measures of FCR are needed. This study aimed to validate the 6-item Cancer Worry Scale (CWS) and to establish a cut-off score for high FCR. METHODS: Participants in this study were 1033 cancer survivors and patients recruited as part of 5 existing studies on FCR involving patients and survivors with gastro-intestinal stromal tumors, colorectal, breast, and prostate cancer. De-identified data of the CWS, Fear of Cancer Recurrence Inventory (FCRI), Impact of Event Scale, Hospital Anxiety and Depression Scale, and EORTC-QLQ-C30 were amalgamated for the analyses. Confirmatory factor analysis of the CWS was performed. Sensitivity and specificity were tested with the FCRI as gold standard. RESULTS: Results confirmed that the 6-item version of the CWS maintained good construct validity, convergent and divergent validity, and high internal consistency (α 0.90). The optimal cut-off for the 6-item CWS was 9 versus 10 using the 12 vs 13 FCRI-SF score (sensitivity 82%, specificity 83%) and the 15 vs 16 FCRI-SF score (sensitivity 88%, specificity 73%). Using the highest FCRI-SF cut-off (21 vs 22), the optimal CWS cut-off was 11 vs 12 (sensitivity 88%, specificity 81%). CONCLUSIONS: The present results provide researchers and clinicians with a brief valid and reliable measure of FCR which is suitable for measuring FCR in cancer patients and survivors.
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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 | 0.017 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".