The FCR‐1: Initial validation of a single‐item measure of fear of cancer recurrence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it