Adaptation to the Progress in Cancer Genomic Medicine by a Japanese Community Hospital
Bibliographic record
Abstract
Background: Remarkable progress in cancer genomic medicine (CGM) has been made with the advent of next-generation sequencing and advanced computational data analysis approaches. In Japan gene panel testing has been covered by the National Health Insurance System since June 2019. Although Nagoya Memorial Hospital has been designated as a regional medical support hospital, their medical staff are unfamiliar with CGM and generally experience difficulty in explaining the genetic testing to cancer patients. Methods: A multi-disciplinary CGM team was created in July 2019 to adapt to the clinical application of gene panel testing. Hospital functions were then maintained focusing on the following three aspects: a pathology system for handling genetic information, human resource development related to CGM, and a patient support system, including genetic counseling. Results: Third party ISO15189 (International Organization for Standardization) certification was acquired for the Department of Pathology to establish a quality-assured laboratory. Here, we report on 21 cancer patients who consulted and received information from the CGM outpatient department of our hospital. Among them 14 patients were introduced into a group of certified hospitals by the Japanese Ministry of Health, Labour, and Welfare and 10 patients underwent gene panel tests. Conclusions: As a regional medical support hospital dealing with many cancer patients, we will further improve hospital functions to match the progress in CGM.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".