Validity of Self-Reported Comorbidities in Systemic Sclerosis: Table 1.
Bibliographic record
Abstract
OBJECTIVE: To assess the validity of self-reports by patients with systemic sclerosis (SSc) of 5 common, chronic conditions (hypertension, diabetes, cancer, depression, and osteoarthritis/back pain) as compared to chart review. METHODS: SSc patients at a large referral hospital self-reported on a number of comorbidities. Their inpatient and outpatient medical records were abstracted using a standardized data extraction form. Sensitivity, specificity, and positive predictive value of the self-reported diagnoses were calculated using data from the chart review as gold standard. RESULTS: Self-reported comorbidity data were verified by chart review for 130 patients with SSc. The sensitivity of the self-reports for the 5 comorbid conditions was low [range 35% (cancer) to 86% (diabetes)]. The specificity was moderate to high [range 76% (osteoarthritis/back pain) to 99% (cancer)]. The positive predictive values ranged from 31% (depression) to 86% (cancer). CONCLUSION: Self-reports of comorbidities do not provide optimal data for the identification of common, chronic conditions in patients with SSc.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".