Identifying the key characteristics of clinical fear of cancer recurrence: An international Delphi study
Bibliographic record
Abstract
OBJECTIVE: Without an agreed-upon set of characteristics that differentiate clinical from nonclinical levels of fear of cancer recurrence (FCR), it is difficult to ensure that FCR severity is appropriately measured, and that those in need of intervention are identified. The objective of this study was to establish expert consensus on the defining features of clinical FCR. METHOD: A three-round Delphi was used to reach consensus on the defining features of clinical FCR. Sixty-five experts in FCR (researchers, psychologists, physicians, nurses, and allied health professionals) were recruited to suggest and rate potential features of clinical FCR. Participants who indicated they could communicate diagnoses within their clinical role were also asked to consider the application of established DSM-5 and proposed ICD-11 diagnostic criteria (Health Anxiety, Illness Anxiety Disorder, Somatic Symptom Disorder) to clinical FCR. RESULTS: Participants' ratings suggested that the following four features are key characteristics of clinical FCR: (a) high levels of preoccupation; (b) high levels of worry; (c) that are persistent; and (d) hypervigilance to bodily symptoms. Of participants whose professional role allowed them to diagnose mental disorders, 84% indicated it would be helpful to diagnose clinical FCR, but the use of established diagnostic criteria related to health anxiety or somatic-related disorders to clinical FCR was not supported. This suggests that participants consider clinical FCR as a presentation that is specific to cancer survivors. CONCLUSION: Clinical FCR was conceptualized as a multidimensional construct. Further research is needed to empirically validate the proposed defining features.
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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 | 0.057 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".