PO.6.125 To have butterflies in one’s . . . medical report!
Bibliographic record
Abstract
Purpose Diagnosis and treatment of systemic lupus erythematosus (SLE) are based on the compilation ofcomplex sets of clinical data, often established over several years. Reading through those large matrices canbe time-consuming and require specifi c training. Thus, our aim was to create a visual representation thatmakes it easier to access the global health status of an SLE patient, and to use it as a knowledge transfertool. Methods First, we selected clinical criteria that are representative, useful, and revealing of the medicalsituation in SLE. Using R language programming, we developed a script that automatically transposesclinical data into an attractive image, whose graphical characteristics refl ect the selected clinical criteria. Results The visual representation is a butterfl y, the emblematic symbol of lupus, which incorporates shadesof purple, the color of lupus awareness, and is compliant for people with color blindness. The visualgraphically provides eleven key clinical criteria, including patient-reported outcomes: age, sex, diseaseactivity (SLE Disease Activity Index 2000), organ damage (Systemic Lupus International Collaborating ClinicsDamage Index), comorbidities (Charlson Comorbidity Index), physical- and mental-health-related quality oflife (component summary scores from the 36-Item Short Form Survey), medication with antimalarial drugs,immunosuppressants, biologics and dosages of prednisone. Conclusions We implemented an automated tool that transposes complex and heterogeneous clinical dataobtained from patients with SLE, into an intuitive visual medium. In addition to helping physicians to rapidlycomprehend the health status of SLE patients, this data visualization shall facilitate communication betweenphysicians, scientists, patients, and the public in general. Also, we believe it could help patients takeownership of their own condition, raise public awareness about SLE, and act as an incentive to furtherinvolve patients in research.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.871 | 0.746 |
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".