Response to <i>Comment on “Reflex somatic testing for the detection of FGFR alterations in urinary tract carcinomas: a dual-institutional experience”</i>
Bibliographic record
Abstract
Dear correspondence author(s), We would like to thank you for your interest in our study reporting our experience with reflex FGFR somatic testing in urinary tract carcinomas.1 Published results were based on real-world data based on a publicly funded cancer biomarker testing program, where FGFR molecular testing was performed on a single specimen per patient with advanced/metastatic urinary tract carcinoma. For each patient, the most recent specimen with sufficient tumor DNA, based on tumor area and cellularity, was selected for molecular testing. Pathologist-guided macrodissection was performed on the most representative block containing all variant histologies. Distinct molecular testing of areas showing different variant histologies via laser microdissection would certainly enhance morphologic-molecular correlations. Molecular characterization of all primary and metastatic specimens from each patient, as well as sampling at multiple time points during therapy, would also better inform us about tumor evolution, resistance mechanisms arising during therapy, and potential microenvironmental influences on the tumor molecular profile. We have indeed acknowledged that these represent the main limitations of our study. While not feasible in a publicly funded health care system, these strategies would certainly be appropriate in a hypothesis-driven research setting, allowing for a more comprehensive analysis of the disease.
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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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.028 | 0.026 |
| Insufficient payload (model declined to judge) | 0.014 | 0.014 |
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".