Bibliographic record
Abstract
In the era of precision oncology, major strides are being made to use individual tumor information for clinical decision-making. Differing from traditional biopsy methods, the emerging practice of liquid biopsy provides a minimally invasive way of obtaining tumor cells and derived molecules. Liquid biopsy provides a means to detect and monitor disease progression, recurrence, and treatment response in a noninvasive way, and to potentially complement classical biopsy. Uveal melanoma (UM) is a unique malignancy, with diagnosis heavily reliant on imaging, few repeat biopsies, and a high rate of metastasis, which occurs hematogenously and often many years after diagnosis. In this disease setting, a noninvasive biomarker to detect, monitor, and study the disease in real time could lead to better disease understanding and patient care. While advances have been made in the detection of tumor-disseminated components, sensitivity and specificity remain important challenges. Ambiguity remains in how to interpret current findings and in how liquid biopsy can have a place in clinical practice. Related publications in UM are few compared to other cancers, but with further studies we may be able to uncover more about the biology of disseminated molecules and the mechanisms involved in the progression to metastasis.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".