Spotlight on the Fear of Cancer Recurrence Inventory (FCRI)
Bibliographic record
Abstract
Fear of cancer recurrence (FCR) is a pervasive concern for people living with cancer. The rapidly expanding FCR literature has been weakened somewhat by use of miscellaneous FCR measures of varying quality. The Fear of Cancer Recurrence Inventory (FCRI) has been widely used in observational and intervention studies and the FCRI severity subscale, also known as the FCRI-Short Form (FCRI-SF), is often used to identify potential cases of clinically significant FCR. Given the FCRI's increasing use in research and clinical practice, we aimed to provide an overview, critique, and suggested improvements of the FCRI. Studies citing the original FCRI validation paper were identified and synthesised using narrative and meta-analytic methods. The 42-item FCRI has demonstrated a reasonably robust 7-factor structure across evaluations in multiple languages, although certain subscales (eg, Coping) demonstrate sub-optimal reliability. Confirmation of the cross-cultural equivalence of several FCRI translations is needed. Meta-analysis of FCRI-SF scores revealed a combined weighted mean score of 15.7/36, a little above the lowest proposed cut-off score (≥13) for clinical FCR. Depending on the FCRI-SF cut-off used, between 30.0% and 53.9% of the cancer population (ie, patients and survivors) appear to experience sub-clinical or clinical FCR. Higher FCRI scores were associated with younger age and female gender, pain/physical symptoms and psychological morbidity, consistent with the FCR literature generally. Issues regarding the application and interpretation of the FCRI remain. Whether the FCRI is well suited to assessing fear of progression as well as recurrence is unclear, the meaningfulness of the FCRI total score is debatable, and the use of the FCRI-SF to screen for clinical FCR is problematic, as items do not reflect established characteristics of clinical FCR. Refinement of the FCRI is needed for it to remain a key FCR assessment tool in future research and clinical practice.
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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 | 0.038 | 0.086 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".