Growing indication for FNA to study and analyze tumor heterogeneity at metastatic sites
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In routine practice, suspected metastases in patients with cancer are only occasionally biopsied, primarily because of the cost and invasiveness of the procedure. However, biopsies of metastatic lesions can be valuable, not only in confirming the presence of metastatic disease, but also in revealing unsuspected benign disease or secondary malignancies. In addition, such biopsies also allow the assessment of biomarkers that might differ from those on primary tumor cells, and can thereby facilitate selection of the optimal treatment. Because of the increasing recognition of clonal and phenotypic heterogeneity of tumors, we anticipate that in the near future, biopsying of metastatic lesions will constitute a standard-of-care practice, allowing assessment of molecular differences between the primary tumor and metastatic lesions. In our opinion, fine-needle aspiration is currently the best method for making repeated biopsies to monitor the tumor: it is minimally invasive, safe, and cost effective and can be coupled with modern ancillary techniques. Here we provide an up-to-date review of the clinical implications of tumor heterogeneity in metastatic disease and the ancillary molecular techniques used in cytology; we also discuss the role of modern cytology in contemporary diagnosis and management of metastatic cancer.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it