Health‐related quality‐of‐life assessment of patients with solid tumors on immuno‐oncology therapies
Bibliographic record
Abstract
Immuno-oncology therapies have been approved for various solid tumors; however, the high cost of these treatments and their potential toxicities require a thorough assessment of their risks and benefits. Collection of data directly from patients through patient-reported outcome instruments can improve the precision and reliability of adverse event detection, assess tolerability of adverse events, and provide an evaluation of health-related quality of life (HRQOL) changes from immuno-oncology therapies. There is robust development in HRQOL tools specifically for patients treated with immuno-oncology agents. This review examines the history and basic concepts of HRQOL and patient-reported outcome assessments commonly used in oncological trials, highlighting the strengths and weaknesses of current approaches when applied to immunotherapies, as well as some of the current efforts to develop tools for this field and opportunities for future research. LAY SUMMARY: Immuno-oncology (IO) therapies are costly and carry potential toxicities known as immune-related adverse events. Evaluation of health-related quality of life (HRQOL) can impact the risk-benefit assessment of IO therapies. Integration of HRQOL end points and patient-reported outcome data for IO therapies are urgently needed. Ongoing robust development of patient-reported outcome tools specific to IO therapies are currently underway and will permit the evaluation of HRQOL for IO agents. Improvement in precision and reliability of HRQOL evaluation will enhance the ultimate true value of these expensive and effective drugs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".