MP286NATURAL HISTORY FOR ADULT PATIENTS WITH SPORADIC ANGIOMYOLIPOMA IN THE NETHERLANDS
Bibliographic record
Abstract
Introduction and Aims: Renal angiomyolipomata (AML) are nonmalignant, highly-vascularizedlesions accounting for about 2-6 % of kidney tumors. While sometimes associated with tuberous sclerosis complex (TSC), AML present sporadically in about 80% of cases. Sporadic AML (sAML) can grow over time, may be associated with complications such as chronic kidney disease (CKD), spontaneous renal hemorrhages, hypertension, and lymphangioleiomyomatosis (LAM), and can necessitate radical interventions such as nephrectomy. This study describes clinical characteristics and the extent of renal dysfunction of a cohort of patients (pts) with sAML. Methods: This was a retrospective, longitudinal cohort study conducted using medical chart data from pts with sAML treated at the University Medical Center - Utrecht, a major specialty center in the Netherlands, from 1995 to 2015. The data combined demographic information with records from scans, specialist visits, medications, and other health care services. Pt characteristics at diagnosis (age, gender, number and size of AML, prior AML bleeding, and LAM) and outcomes during follow-up (hypertension, use of mammalian target of rapamycin [mTOR] inhibitors, embolization, and nephrectomy) were reported and stratified into cohorts based on the longest diameter of their largest AML at diagnosis (<3.5 cm vs. ≥3.5 cm). Longitudinal kidney function measurements (eGFR) were plotted by age and compared with a cohort of pts with TSC and with the general Dutch population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".