Patterns of Concerns Among Hematological Cancer Survivors
Bibliographic record
Abstract
BACKGROUND: Advances in treatment for hematological cancers warrant greater attention on survivorship concerns. OBJECTIVE: The aims of this study were to describe survivorship concerns among hematological cancer survivors, identify subgroups of survivors with distinct classes of concerns, and examine sociodemographic and clinical differences across subgroups. METHODS: We conducted a cross-sectional analysis of data from 1160 hematological cancer survivors, who rated their degree of concern regarding 20 physical, emotional, and practical changes. Clusters of concerns were identified using latent class analysis. Associations between respondent characteristics and cluster membership were calculated using multinomial logistic regression. RESULTS: Survivors had a mean of 7.5 concerns (SD, 4.6; range, 0-19), the most frequent being fatigue/tiredness (85.4%); anxiety, stress, and worry about cancer returning (70.2%); and changes to concentration/memory (55.4%). Three distinct classes of concerns were identified: class 1 (low, 47.0%), characterized by low endorsement of most concerns, apart from fatigue; class 2 (moderate, 32.3%), characterized by high endorsement of a combination of concerns across domains; and class 3 (high, 20.7%), characterized by the highest number of concerns out of the 3 identified classes, including greater endorsement of concerns relating to sexual well-being. Class membership was differentiated by survivor age, sex, marital status, and diagnosis. CONCLUSIONS: Three distinct patterns of concerns were detected in a large sample of hematological cancer survivors. Patterns of concerns could be differentiated by survivor characteristics. IMPLICATIONS FOR PRACTICE: Our study highlights the concerns experienced by hematological cancer survivors and provides support for a tailored biopsychosocial approach to survivorship care in this context.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".