Development and internal validation of a clinical risk score to predict incident renal and pulmonary tumours in people with tuberous sclerosis complex
Bibliographic record
Abstract
OBJECTIVE: This study aims to develop and internally validate a clinical risk score to predict incident renal angiomyolipoma (AML) and pulmonary lymphangioleiomyomatosis (LAM) in people with tuberous sclerosis complex (TSC). STUDY DESIGN: Data from 2420 participants in the TSC Alliance Natural History Database were leveraged for these analyses. Logistic regression was used to predict AML and LAM development using 10 early-onset clinical manifestations of TSC as potential predictors, in addition to sex and genetic mutation. For our models, we divided AML into three separate outcomes: presence or absence of AML, unilateral or bilateral and whether any are ≥3 cm in diameter. The resulting regression models were turned into clinical risk scores which were then internally validated using bootstrap resampling, measuring discrimination and calibration. RESULTS: The lowest clinical risk scores predicted a risk of AML and LAM of 1% and 0%, while the highest scores predicted a risk of 99% and 73%, respectively. Calibration was excellent for all three AML outcomes and good for LAM. Discrimination ranged from good to strong. C-statistics of 0.84, 0.83, 0.83 and 0.92 were seen for AML, bilateral AML, AML with a lesion≥3 cm and LAM, respectively. CONCLUSION: Our work is an important step towards identifying individuals who could benefit from preventative strategies as well as more versus less frequent screening imaging. We expect that our work will allow for more personalised medicine in people with TSC. External validation of the risk scores will be important to confirm the robustness of our findings.
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".