What do these scores mean? Presenting patient‐reported outcomes data to patients and clinicians to improve interpretability
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes (PROs) (eg, symptoms, functioning) can inform patient management. However, patients and clinicians often have difficulty interpreting score meaning. The authors tested approaches for presenting PRO data to improve interpretability. METHODS: This mixed-methods study included an Internet survey of cancer patients/survivors, oncology clinicians, and PRO researchers circulated via snowball sampling, plus individual in-person interviews. Clinical importance was conveyed using 3 approaches (presented in random order): normal score range shaded green, concerning scores circled in red, and red threshold lines indicating normal versus concerning scores. Versions also tested 2 approaches to score directionality: higher = more (better for function, worse for symptoms) and higher = better for both function and symptoms. Qualitative data from online comments and in-person interviews supplemented quantitative results on interpretation accuracy, clarity, and the "most useful" format. RESULTS: The survey included 1113 respondents: 627 survivors, 236 clinicians, and 250 researchers, plus 10 patients and 10 clinicians who were purposively sampled interviewees. Interpretation accuracy ranged from 53% to 100%. The formats in which higher = better were interpreted more accurately versus those in which higher = more (odds ratio [OR], 1.30; 95% confidence interval [CI], 1.07-1.58) and were more likely to be rated "very"/"somewhat" clear (OR, 1.39; 95% CI, 1.13-1.70) and "very" clear (OR, 1.36; 95% CI, 1.18-1.58). Red circle formats were interpreted more accurately than green-shaded formats when the first format presented (OR, 1.29; 95% CI, 1.00-1.65). Threshold-line formats were more likely to be rated "very" clear than green-shaded (OR, 1.43; 95% CI, 1.19-1.71) and red-circled (OR, 1.22, 95% CI, 1.02-1.46) formats. Threshold lines were most often selected as "most useful." CONCLUSIONS: The current results support presenting PRO data with higher = better directionality and threshold lines indicating normal versus concerning scores. Cancer 2017;123:1848-1859. © 2017 The Authors. Cancer published byWiley Periodicals, Inc. on behalf of American Cancer Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations aremade.
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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 | 0.144 | 0.436 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".