The impact of everolimus on hematologic parameters in patients with renal angiomyolipoma associated with tuberous sclerosis complex
Bibliographic record
Abstract
BACKGROUND: Everolimus is an effective treatment for renal angiomyolipoma associated with TSC (TSC-RAML). However, its impact on hematologic parameters in TSC-RAML patients remains unclear. METHODS: Hematologic data were collected from TSC-RAML patients undergoing everolimus treatment in two registered clinical trials. Dynamic changes in hematologic parameters during treatment were analyzed. Additionally, we also explored variations in hematologic impact based on gender and age within the patient population. RESULT: A total of 55 patients from the two clinical trials are included in this analysis. Hemoglobin, white blood cells (WBC), lymphocytes, neutrophils, and platelet showed significant decreases during everolimus treatment (P < 0.05). However, the decline in hemoglobin, WBC, and neutrophils attenuated by the 12th month (P ≥ 0.05). Aspartate transaminase (AST), Alanine transferase (ALT), total cholesterol (TC), and triglyceride (TG) increased significantly during everolimus treatment (P < 0.05), and these increases persisted throughout the year-long treatment. Hemoglobin decreased significantly more in male patients (- 15 vs - 6, P = 0.010), and AST showed a more significant increase in males (7.0 vs 3.0, P = 0.041). Platelet counts decreased significantly more in younger patients (≤ 30 years old) compared to older patients (- 50 vs - 14, P = 0.020). CONCLUSION: Everolimus administration in TSC-RAML patients may increase hematologic risks, with male and younger patients potentially exhibiting greater susceptibility to these effects.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".