Tabular, Annotated, Visual, or Trends + Contextual Information? Preferences for Online Laboratory Results Displays
Bibliographic record
Abstract
People are increasingly offered access to their personal health information (e.g., laboratory results, clinical notes, diagnostic imaging results). However, this information is the same as that used by health care providers with clinical expertise and training in medical terminology, which citizens typically do not have. In this study, we examined participants (N = 24) preferences for four different types of displays for online laboratory (lab) results: Tabular, Annotated, Visual, and Trends + Contextual Information. The Friedman test of difference comparing participants' ratings of the four displays was significant, χ2(3)=10.8, P=.013, and the Wilcoxon signed rank pairwise comparison tests revealed that participants rated the visual lab results display significantly more favourably than the traditional display (Z=-2.746, P=.006). These findings indicate that many people prefer lab results displayed using more visual cues and some perceived this format as easier to understand than the other display formats. Given the importance of people accessing, understanding, and using their own health information, it is crucial for displays and systems to provide a better user experience. Displaying data (e.g., lab results) visually is one possible way to improve interpretability of personal health information provided to the public.
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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 | 0.004 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".