Communicating Quality of Life Information to Cancer Patients: A Study of Six Presentation Formats
Bibliographic record
Abstract
PURPOSE: To determine which formats for presenting health-related quality of life (HRQL) data are interpreted most accurately and are most preferred by cancer patients. Patients often want a great deal of information about cancer treatments, including information relevant to HRQL. Clinical trials provide methodologically sound HRQL data that may be useful to patients. PATIENTS AND METHODS: In a multicenter study, 198 patients with previously treated cancer participated in a structured interview. Participants judged HRQL information presented in one textual and five graphical formats. Outcome measures included the accuracy of patients' interpretations and ease-of-use and helpfulness ratings for each format. RESULTS: Correct interpretations ranged from 85% to 98% across formats (F = 10.3, P < .0001) with line graphs of mean HRQL scores over time being interpreted correctly most often. Older patients and less-educated patients were less likely to interpret graphs accurately (F = 7.3, P = .008; and F = 10.6, P = .001, respectively), but all groups were most accurate on simple line graphs. Multivariate analysis revealed that format type, participant age and education were independent predictors of accuracy rates. Patients' ratings also varied across formats both for ease of understanding scores (F = 12.1, P < .0001) and for helpfulness scores (F = 13.2, P < .0001), with line graphs being rated highest on both outcomes. CONCLUSION: Patients generally prefer a simple linear representation of group mean HRQL scores, and can accurately interpret data presented in this format more than 98% of the time irrespective of their age group and educational level. The findings have important implications for the communication of clinical trial HRQL results.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".