Validity and screening capacity of the FCR-1r for fear of cancer recurrence in long-term colorectal cancer survivors
Bibliographic record
Abstract
PURPOSE: Existing fear of cancer recurrence (FCR) screening measures is being shortened to facilitate clinical use. This study aimed to evaluate the validity and screening capacity of a single-item FCR screening measure (FCR-1r) in long-term colorectal cancer (CRC) survivors with no recurrence and assess whether it performs as well in older as in younger survivors. METHODS: All Danish CRC survivors above 18, diagnosed and treated with curative intent between 2014 and 2018, were located through a national patient registry. A questionnaire including the FCR-1r, which measures FCR on a 0-10 visual analog scale, alongside the validated Fear of Cancer Recurrence Inventory Short Form (FCRI-SF) as a reference standard was distributed between November 2021 and May 2023. Screening capacity and cut-offs were evaluated with a receiver-operating characteristic analysis (ROC) in older (≥ 65 years) compared to younger (< 65 years) CRC survivors. Hypotheses regarding associations with other psychological variables were tested as indicators of convergent and divergent validity. RESULTS: Of the CRC survivors, 2,128/4,483 (47.5%) responded; 1,654 (36.9%) questionnaires were eligible for analyses (median age 76 (range 38-98), 47% female). Of the responders, 85.2% were aged ≥ 65. Ninety-two participants (5.6%) reported FCRI-SF scores ≥ 22 indicating clinically significant FCR. A FCR-1r cut-off ≥ 5/10 had 93.5% sensitivity and 80.4% specificity for detecting clinically significant FCR (AUC = 0.93, 95% CI 0.91-0.94) in the overall sample. The discrimination ability was significantly better in older (AUC = 0.93, 95% CI 0.91-0.95) compared to younger (0.87, 95% (0.82-0.92), p = 0.04) CRC survivors. The FCR-1r demonstrated concurrent validity against the FCRI-SF (r = 0.71, p < 0.0001) and convergent validity against the short-versions of the Symptom Checklist-90-R subscales for anxiety (r = 0.38, p < 0.0001), depression (r = 0.27, p < 0.0001), and emotional distress (r = 0.37, p < 0.0001). The FCR-1r correlated weakly with employment status (r = - 0.09, p < 0.0001) and not with marital status (r = 0.01, p = 0.66) indicating divergent validity. CONCLUSIONS: The FCR-1r is a valid tool for FCR screening in CRC survivors with excellent ability to discriminate between clinical and non-clinical FCR, particularly in older CRC 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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".