Development and testing of a decision board to help clinicians present treatment options to lupus nephritis patients in Brazil
Bibliographic record
Abstract
OBJECTIVE: Decision boards (DBs) help clinicians present options and include patients in the decision-making process. Our objective was to describe the steps to develop a DB to support shared decision making and assess reliability and construct validity. METHODS: Systemic lupus erythematosus (SLE) scenarios were designed with the support of experts for disease severity, potential side effects, and outcomes. The DB comprised clinical information, 2 different treatment options (oral and intravenous), a description of the potential to control SLE within 5 years, and a list of potential side effects. Patients selected what they thought would be the 3 worst side effects and were informed of the probability that these would occur. We presented the DB to 172 patients who were asked to select and justify 1 treatment option. Reliability was assessed by kappa statistics. Construct validity was tested by an a priori hypothesis, analyzing the correlation between treatment decision and side effects selected, self-assessment score, educational level, and clinical aspects. RESULTS: Patients favored oral medication, and side effects most often listed were iatrogenic cancer (44.2%), hair loss (21.6%), and severe infection (19.1%). Justifications were risk (48.9%), practicality (36.6%), effectiveness (12.2%), and risk-benefit tradeoff (2.3%). Reliability was similar to that found in the test phase (kappa = 0.689, P < 0.001). Validity was tested by prediction of treatment decision based on the undesirable side effects selected (P = 0.047). DB content was clear and easy for all patients to understand (P = 0.05). Immunosuppressive drugs influenced patient decisions (P = 0.006). CONCLUSION: DB is a reliable and valid instrument to assess SLE patient preference.
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 | 0.036 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".