Evaluating cancer patient–reported outcome measures: Readability and implications for clinical use
Bibliographic record
Abstract
BACKGROUND: The benefits of patient-reported outcome measures (PROMs) are well known; however, their readability has come into question because multiple PROMs have been found to be incomprehensible to patients. This is a critical safety and equity consideration because PROMs are increasingly being integrated into routine clinical practice. A key strategy for promoting patient comprehension is the use of plain language. The aim of this study was to determine whether PROMs routinely used in the cancer setting meet plain-language best practices. METHODS: To report the plain-language level of each PROM, readability (Fry Readability Graph, Simple Measure of Gobbledygook, Flesch Reading Ease, and FORCAST) and understandability assessments (Patient Education Materials Assessment Tool [PEMAT] for Printable Materials) were performed. PROMs at grade level 6 or lower and with PEMAT scores greater than 80% were considered to meet plain-language best practices. PROMs were divided into 4 domains (physical, emotional, social, and quality of life) and 17 dimensions (eg, pain was a dimension of the physical domain). A subanalysis was conducted to determine whether specific domains and dimensions were more likely to adhere to plain-language best practices. RESULTS: More than half of the 45 PROMs evaluated (n = 33 [73%]) had a grade level higher than 6. Understandability scores ranged from 29% to 100%. The majority of the PROMs that did not meet plain-language best practices were within the physical and emotional domains and focused on the patient's symptom experience. CONCLUSIONS: This evaluation shows that more than half of the most commonly used cancer PROMs do not meet plain-language best practices. Practice implications include the necessity for plain-language assessment during the PROM validation process, the consideration of plain language in PROM selection, and plain-language review and editing of low-scoring PROMs.
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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 | 0.098 | 0.269 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".