Fear of cancer recurrence among patients and survivors diagnosed with localized renal cell carcinoma.
Bibliographic record
Abstract
649 Background: Fear of cancer recurrence (FCR) is considered one of the most pervasive and burdensome sources of distress for patients. Whereas it can be considered an adaptive response to real threats associated with diagnosis, treatment and disease, numerous studies have suggested that elevated levels can be dysfunctional. Despite this extensive body of research, little is known regarding FCR among individuals diagnosed with renal cell carcinoma (RCC). The current study sought to describe the prevalence of FCR among patients with RCC. Methods: Patients with localized RCC participated in an international survey from 07/19 to 09/19, through an online platform devised by a non-profit patient advocacy group (KCCure). Patients were assessed for FCR using the FCR-7, a 7-item measure, ranging from 0 to 28. A cutoff score of 17 indicates a moderate level of FCR, while a cutoff of 27 indicates severe level of FCR. Linear regression was used to determine the association between FCR and patients’ characteristics. Results: A total of 1150 patients participated in this survey, of which 412 had localized disease and were assessed for FCR. The majority were female (79%), with a median age of 54 years old (range, 30-80), and well-educated (58%). Patients were predominantly from US (85%), Canada (4%) and Germany (2%) and lived in suburban (48%) or rural areas (32%). More than half of participants were diagnosed with disease stage I (56%) and the remainder were divided between stage II (19%) and III (24%). More than half of patients (55%) reported a moderate or severe level of FCR. Younger patients (p=0.001) and those of female gender (p=0.004) were more likely to report higher levels of FCR. Conclusions: To our knowledge, this is the first study to quantify the degree of FCR among patients and survivors diagnosed with localized RCC. Importantly, high rates of FCR were associated with female gender and younger age, possibly driven by the fact that women may be more open to disclosing emotional symptoms and younger patients are still to pass through many life milestones and thus fear may be more pronounced. Targeted assessment and interventions are needed to address this highly prevalent form of distress among those diagnosed with RCC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".