Development of the Functional Assessment of Cancer Therapy–Immune Checkpoint Modulator (FACT‐ICM): A toxicity subscale to measure quality of life in patients with cancer who are treated with ICMs
Bibliographic record
Abstract
BACKGROUND: Patients with cancer who are treated with immune checkpoint modulators (ICMs) have their health-related quality of life (HRQOL) measured using general patient-reported outcome (PRO) tools. To the authors' knowledge, no instrument has been developed to date specifically for patients treated with ICMs. The objective of the current study was to develop a toxicity subscale PRO instrument for patients treated with ICMs to assess HRQOL. METHODS: Input was collected from a systematic review as well as patients and physicians experienced with ICM treatment. Descriptive thematic analysis was used to evaluate the qualitative data obtained from patient focus groups and interviews, which informed an initial list of items that described ICM side effects and their impact on HRQOL. These inputs informed item generation and/or reduction to develop a toxicity subscale. RESULTS: Focus groups and individual interviews with 37 ICM-treated patients generated an initial list of 176 items. After a first round of item reduction that produced a shortened list of 76 items, 16 physicians who care for patients who are treated with ICMs were surveyed with a list of 49 patient-reported side effects and 11 physicians participated in follow-up interviews. A second round of item reduction was informed by the physician responses to produce a list of 25 items. CONCLUSIONS: To the authors' knowledge, this 25-item list is the first HRQOL-focused toxicity subscale for patients treated with ICMs and was developed in accordance with US Food and Drug Administration guidelines, which prioritize patient input in developing PRO tools. The subscale will be combined with the Functional Assessment of Cancer Therapy-General (FACT-G) to form the FACT-ICM. Prior to recommending the formal use of this PRO instrument, the authors will evaluate its validity and reliability in longitudinal studies involving substantially more patients.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".