A new landscape in illness uncertainty: A systematic review and thematic synthesis of the experience of uncertainty in patients with advanced cancer receiving immunotherapy or targeted therapy
Bibliographic record
Abstract
OBJECTIVE: Over the past 20 years, immunotherapy and targeted therapy (TT) have been extending the life expectancy and providing hope for a growing number of patients with advanced and metastatic cancer. However, the efficacy, side effects, and overall prognosis of these treatments are highly unpredictable. Recent research suggests that these patients may be experiencing significant uncertainty which impacts their functioning. This study reviewed the literature on the experiences of uncertainty for individuals with advanced or metastatic cancer patients who are receiving immunotherapy or TT. METHOD: A systematic literature review was conducted. Data was extracted from studies by pairs of reviewers. Literature quality was appraised using the Critical Appraisal Skills Program checklist. Following data extraction, thematic synthesis was used to summarize findings across studies and generate overarching themes. RESULTS: Fifteen qualitative studies were included. Findings highlighted impacts of various sources of uncertainty (financial, emotional, social), unmet needs related to uncertainty (practical, informational, communication), and recommendations for the management of uncertainty. Clinical implications and study limitations were indicated. CONCLUSIONS: Findings were situated within Mishel's Uncertainty in Illness Theory and the literature on supportive care for advanced cancer populations. Recommendations related to improving healthcare provider communication and balancing hope and expectations for treatment outcomes were highlighted. Further research is needed to investigate experiences of uncertainty in this population. Tailored interventions for uncertainty may be warranted.
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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 | 0.027 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| 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".