The FCR‐1: Initial validation of a single‐item measure of fear of cancer recurrence
Bibliographic record
Abstract
OBJECTIVE: Fear of cancer recurrence (FCR) is characterized by the fear, worry or concern that cancer will come back or progress. The negative effects associated with FCR are consistently identified by cancer survivors as one of their most prominent unmet needs. Current measures of FCR can be long, complex and burdensome for survivors to complete. The objective of the present study is to develop and validate a one-item measure of FCR. METHODS: The ability of the FCR-1 to detect change in FCR over time was analyzed using a repeated-measures ANOVA and paired-samples t-tests. Pearson correlations were used to measure the concurrent, convergent and discriminant validity of the FCR-1, and a ROC analysis was conducted to determine an optimal clinical cut-off score. RESULTS: The FCR-1 was found to be responsive to change in FCR over time. It demonstrated concurrent validity with the FCRI (r = .395, P = .010), and convergent validity with the Mishel Uncertainty in Illness Scale (r = .493, P = .001) and the Reassurance Questionnaire (r = .325, P = .044). Discriminant validity was confirmed when the FCR-1 did not significantly correlate with unrelated measures. A ROC analysis pinpointed an optimal clinical cut-off score of 45.0. CONCLUSIONS: The FCR-1 is a promising tool that can be incorporated in clinical and research settings. Due to its brevity, the care needs of highly distressed patients can be met quickly and efficiently. In research settings, the FCR-1 can reduce the cognitive burden experienced by 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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".