KRAS Oncogene Mutations in Colorectal Cancer Patients in a Nepalese Tertiary Care Hospital
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer is one of the most common cancers in the world and ranks among top ten cancer in Nepal. Limited data have been reported in the literature regarding the prevalence of Kristen Rat Sarcoma viral oncogene mutation in Nepalese patients with colorectal cancer. In a low income country such as Nepal where majority of cancer patient pay for treatment out-of-pocket, it is important to ascertain Kristen Rat Sarcoma viral oncogene mutation status before starting treatment with these agents. METHODS: We analysed 22 colorectal cancer specimens diagnosed histopathologically. Real Time Polymerase Chain Reaction was performed on extracted DNA using RoterGene from Qiagen. US Food and Drug Administration approved kit was used for detection of Kristen Rat Sarcoma viral oncogene mutation i.e. TheraScreen: K-RAS Mutation Kit: The K-RAS Kit detects seven Kristen Rat Sarcoma viral oncogene mutations in codons 12 and 13 of the Kristen Rat Sarcoma viral oncogene. RESULTS: Kristen Rat Sarcoma viral oncogene mutation was observed in 13 (59%) of the samples studied. All samples had point mutation on codons 12 while 5 samples (38%) also had a point mutation on codons 13. No association was found between the presence of Kristen Rat Sarcoma viral oncogene mutation and gender or age or sidedness of the cancer. CONCLUSIONS: Kristen Rat Sarcoma viral oncogene was commonly present in colorectal cancer specimens. Further efforts towards establishment of diagnostic test, generation of new database, development and scale up of laboratory services are needed throughout the nation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".