Validation of self-reported race in PROMIS
Bibliographic record
Abstract
Race is an important determinant of renal outcomes but is not validated in most administrative data. We validated self-reported race (SRR) in PROMIS using a telephone surveys as gold standard and compared PROMIS SRR to the Canadian census classification of race.PROMIS SRR had an accuracy of 95.3% (CI 94.2-97.0%) when validated against the survey with Sn and Sp u226590% in all race groups except in Aboriginals (Sn 87.5%). The positive and negative predictive values were u226595%. The Canadian census had overall accuracy of 95.7% (CI 94.4-97.6%) when validated against PROMIS SRR with Sn and Sp u226590%. The results did not differ in subgroups based on age, sex, birth outside Canada, or renal group.We validate PROMIS SRR for use in the secondary analysis of administrative data for research. PROMIS also correlates with census race categories allowing linkage with other data sources that use census-based definitions of race.
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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 | 0.044 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".