Computational extraction and analysis of de-identified medical records to characterize hyperammonemia in patients with fibrolamellar carcinoma (FLC).
Bibliographic record
Abstract
e16169 Background: Rare cancers including FLC make up 25% of adult tumors, but are difficult to study due to low incidence and incomplete case identification. FLC occurs in adolescents and young adults without liver dysfunction. Hyperammonemia has been frequently reported in FLC patients, but is poorly understood. Methods: Data from three clinical trials allowed us to establish the incidence of hyperammonemia (serum ammonia value > 75 µmol/L) in FLC. In these studies, FLC patients received everolimus, estrogen deprivation therapy (EDT) with leuprolide + letrozole or everolimus + EDT (Oncologist. 2020 25(11):925-e1603), ENMD-2076 (Oncologist. 2020 25(12):e1837-e1845), or neratinib (J Clin Oncol 39, 2021 (suppl 3; abstr 310); ammonia was tested prospectively in the latter two studies. To assess impacts of cancer therapy or liver dysfunction, we studied hyperammonemia in FLC and non-FLC patients at UCSF in parallel. Using Natural Language Processing (NLP) of pathology reports and oncology notes from > 2300 liver cancer patients from the last 12 years of UCSF records, we identified a cohort of patients with FLC, contrasting their laboratory data to all UCSF patients with ammonia testing for the last 10 years. We used leiden clustering and umap dimensionality reduction to contrast FLC and other patients to assess the clinical context of hyperammonemia. Results: Data from the 3 trials showed hyperammonemia in 10 of 32 (31.3%) FLC patients during study participation, independent of the therapy received. These patients exhibited hyperammonemia with varying levels, and at different points in their treatment. NLP identified 37 patients with FLC ( < 0.1% of liver cancer patients), with 33% showing hyperammonemia. Across all UCSF patients, we found 24,000 independent visits where ammonia was tested, with > 2400 demonstrating hyperammonemia. Using leidan clustering on all encounters with ammonia > 75 µmol/L, we found distinct subsets of hyperammonemia corresponding to known metabolic and physiologic processes (e.g., fulminant liver failure, tumor lysis syndrome, etc). FLC patients clustered separately from hepatocellular carcinoma patients with hyperammonemic encephalopathy due to cirrhosis. Conclusions: NLP of large EMRs is a valuable tool to study FLC, a rare cancer. Herein, we have defined hyperammonemia as a frequent event in FLC, not directly linked to hepatic dysfunction or individual therapies. Further investigation may determine whether hyperammonemia is related to FLC tumor biology.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".