Exploring the screening capacity of the Fear of Cancer Recurrence Inventory-Short Form for clinical levels of fear of cancer recurrence
Bibliographic record
Abstract
Objective : Fear of cancer recurrence (FCR) is a common concern among cancer survivors. Identifying survivors with clinically significant FCR requires validated screening measures and clinical cut-offs. We evaluated the Fear of Cancer Recurrence Inventory-Short Form (FCRI-SF) clinical cut-off in 2 samples. \n \nMethods : Level of FCR in study 1 participants (from an Australian randomized controlled trial: ConquerFear) was compared with FCRI-SF scores. Based on a biopsychosocial interview, clinicians rated participants as having nonclinical, subclinical, or clinical FCR. Study 2 participants (from a Canadian FCRI-English validation study) were classified as having clinical or nonclinical FCR by using the semistructured clinical interview for FCR (SIFCR). Receiver operating characteristic analyses evaluated the screening ability of the FCRI-SF against clinician ratings (study 1) and the SIFCR (study 2). \n \nResults : In study 1, 167 cancer survivors (mean age: 53 years, SD = 10.1) participated. Clinicians rated 43% as having clinical FCR. In study 2, 40 cancer survivors (mean age: 68 years, SD = 7.0) participated; 25% met criteria for clinical FCR according to the SIFCR. For both studies 1 and 2, receiver operating characteristic analyses suggested a cut-off ≥22 on the FCRI-SF identified cancer survivors with clinical levels of FCR with adequate sensitivity and specificity. \n \nConclusions : Establishing clinical cut-offs on FCR screening measures is crucial to tailoring individual care and conducting rigorous research. Our results suggest using a higher cut-off on the FCRI-SF than previously reported to identify clinically significant FCR. Continued evaluation and validation of the FCRI-SF cut-off is required across diverse cancer populations.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".