QOL-13. MEASURING FEAR OF CANCER RECURRENCE IN PATIENTS WITH PRIMARY BRAIN TUMORS
Bibliographic record
Abstract
Abstract BACKGROUND Fear of cancer recurrence (FCR) differs for those living with primary brain tumor compared to other cancers due to poorer prognosis, neurologic symptoms, and often lifelong treatment. The gold-standard instrument used to assess FCR–the FCR Inventory (FCRI)–has not been validated in patients with PBT. The present study explored the psychometric properties of the FCRI in patients with brain tumors, with items added assessing brain tumor-specific distress. METHODS Adult patients with brain tumors (n = 87) completed the FCRI and other psychological medical, and demographic questionnaires. Exploratory factor analysis (EFA) was conducted on the FCRI with five additional items informed by neuro-oncology patients and professionals assessing brain tumor-specific hypervigilance (e.g., interpreting headache or cognitive difficulties as possible tumor growth) and one item assessing tendency to research treatments—for a total of 48 items. Correlations investigated convergent/discriminant validity and relationships with relevant medical and demographic variables. RESULTS After iteration, EFA revealed a seven-factor model with 24 retained items, accounting for 68.19% of variance, with factor correlations between .12 and .65. The seven-factor model echoed but did not fully replicate the original FCRI. FCRI-Brain factors were: Triggers, Psychological Distress, Functional Impairments, Insight, Reassurance, Emotion-Focused Coping, and Problem-Focused Coping (Total: Cronbach’s a = .92). The resultant FCRI-Brain demonstrated good convergent validity with measures of depression (r = .57), anxiety (r = .71), and death anxiety (r = .81; ps< .05). Lack of correlation with education (p > .05) supported discriminant validity. There was no relationship with time since diagnosis (p > .05). CONCLUSIONS These data represent the initial validation of an FCR measure in patients with brain tumors. Factor analysis identified a theoretically similar; yet unique seven-factor model for the FCRI-Brain. Item-level exploration will be presented, including further discussion of factor iteration and construction. Future work will build on this data in larger samples and inform intervention development.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".