Feasibility and value of genomic profiling in cancer of unknown primary: real-world evidence from prospective profiling study
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
Real-world evidence regarding the value of integrating genomic profiling (GP) in managing cancer of unknown primary (CUP) is limited. We assessed this clinical utility using a prospective trial of 158 patients with CUP (October 2016-September 2019) who underwent GP using next-generation sequencing designed to identify genomic alterations (GAs). Only 61 (38.6%) patients had sufficient tissue for successful profiling. GAs were seen in 55 (90.2%) patients of which GAs with US Food and Drug Administration-approved genomically matched therapy were seen in 25 (40.9%) patients. A change in therapy was recommended and implemented (primary endpoint of the study) in 16 (10.1%) and 4 (2.5%) patients of the entire study cohort, respectively. The most common reason for inability to implement the profiling-guided therapy was worsening of performance status (56.3%). Integrating GP in management of CUP is feasible but challenging because of paucity of tissue and aggressive natural history of the disease and requires innovative precision strategies.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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