Reliability and Sensitivity of the Self-report of Physician-diagnosed Gout in the Campaign Against Cancer and Heart Disease and the Atherosclerosis Risk in the Community Cohorts
Bibliographic record
Abstract
OBJECTIVE: gout is often defined by self-report in epidemiologic studies. Yet the validity of self-reported gout is uncertain. We evaluated the reliability and sensitivity of the self-report of physician-diagnosed gout in the Campaign Against Cancer and Heart Disease (CLUE II) and the Atherosclerosis Risk in the Community (ARIC) cohorts. METHODS: the CLUE II cohort comprises 12,912 individuals who self-reported gout status on either the 2000, 2003, or 2007 questionnaires. We calculated reliability as the percentage of participants reporting having gout on more than 1 questionnaire using Cohen's κ statistic. The ARIC cohort comprises 11,506 individuals who self-reported gout status at visit 4. We considered a hospital discharge diagnosis of gout or use of a gout-specific medication as the standard against which to calculate the sensitivity of self-reported, physician-diagnosed gout. RESULTS: of the 437 CLUE II participants who self-reported physician-diagnosed gout in 2000, and subsequently answered the 2003 questionnaire, 75% reported gout in 2003 (κ = 0.73). Of the 271 participants who reported gout in 2000, 73% again reported gout at the 2007 followup questionnaire (κ = 0.63). In ARIC, 196 participants met the definition for gout prior to visit 4 and self-reported their gout status at visit 4. The sensitivity of a self-report of physician-diagnosed gout was 84%. Accuracy was similar across sex and race subgroups, but differed across hyperuricemia and education strata. CONCLUSION: these 2 population-based US cohorts suggest that self-report of physician-diagnosed gout has good reliability and sensitivity. Thus, self-report of a physician diagnosis of gout is appropriate for epidemiologic studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".