Diagnostic Accuracy of Clinical Manifestations in Identifying People With Tuberous Sclerosis Complex
Bibliographic record
Abstract
Background and Objectives: and are diagnosed clinically. In such situations, family members cannot be screened using genetic testing. We aimed to establish the diagnostic accuracy of TSC clinical features to better guide the screening process for the families of PwTSC. Methods: We used the TSC Natural History Database, a longitudinal database of PwTSC from 22 North American centers. We used a definite genetic TSC diagnosis as our gold standard. We estimated the sensitivity (95% CI) of TSC-related skin, structural brain, renal, and cardiac manifestations, as well as combinations of these manifestations. Using a series of sensitivity analyses to test alternate assumptions, we estimated positive predictive values and negative predictive values (PPVs and NPVs). Results: Among the 1,300 genetics-positive PwTSC, 50.3% were female and the mean age at diagnosis was 3.7 years. The sensitivity of at least one skin or structural brain manifestation was 98.7% (95% CI 98.0-99.2). The PPV and NPV were 83.2 (95% CI 81.6-84.6) and 98.4% (95% CI 97.8-98.8), respectively, while assuming 50% prevalence and 80% specificity. Including cardiac manifestations marginally increased the sensitivity, PPV, and NPV to 99.5% (95% CI 98.9-99.7), 83.3% (95% CI 81.8-84.7), and 99.4% (95% CI 99.0-99.6), respectively. Other combinations of TSC manifestations had lower or similar diagnostic accuracy. Discussion: Assessment of brain and skin manifestations in family members of genetics-positive PwTSC is sufficient to screen for TSC in most cases, with excellent sensitivity and NPV. Our findings are potentially applicable to family members of genetics-negative PwTSC. Further cardiac screening may optimize diagnostic accuracy in selected cases. Classification of Evidence: This study provides Class IV evidence that a combination of brain and skin manifestations is highly sensitive for diagnosing TSC in family members of patients with genetically confirmed TSC.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".