Understanding the Concept of Uncertainty in Patients With Indolent Lymphoma
Bibliographic record
Abstract
PURPOSE/OBJECTIVES: To review the literature on uncertainty in cancer populations, apply this concept to patients diagnosed with indolent lymphoma, identify sources of uncertainty, and present interventions aimed at assessing and addressing the management of uncertainty. DATA SOURCES: English-language literature related to uncertainty in adult patients with cancer, psychological distress, and non-Hodgkin lymphoma, located through electronic databases PubMed and CINAHL, hand searches, and personal contacts. DATA SYNTHESIS: Review of the literature revealed that uncertainty is being managed in breast cancer survivors and patients with prostate cancer with watchful waiting or active surveillance. The chronic and incurable nature of indolent lymphoma, coupled with symptoms that are vague and transient, are possible sources of uncertainty in patients diagnosed with lymphoma. Nursing interventions should be aimed at assessing, educating, and supporting patients as they work toward a new view of life that incorporates uncertainty. CONCLUSIONS: Literature about the experience of patients diagnosed with lymphoma is lacking. The concept of uncertainty should be recognized by clinicians as an important aspect of living with indolent lymphoma because it is present throughout the disease trajectory and, if left untreated, can have a negative effect on patients' overall quality of life. IMPLICATIONS FOR NURSING: Uncertainty should become an ongoing component of nursing assessment in patients diagnosed with lymphoma. Further research is needed to support the application of uncertainty theory to this patient population.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".