THE ROLE OF A GENOMICS CLINICAL NURSE SPECIALIST IN HELPING TO BUILD A PATHWAY FOR WHOLE GENOME SEQUENCING TO BE OFFERED TO NEURO-ONCOLOGY PATIENTS
Bibliographic record
Abstract
Abstract AIMS Whole genome sequencing is currently being offered to paediatric, teenage and young adult neuro-oncology patients and this was being managed by a genomics practitioner who works cross-site. We found that some patients are being discharged from the hospital without 1 component of the test or the consent. Our aim is to create a solid pathway in which fresh tissue, blood sample and record of discussion are taken at a certain stage in their hospital admission. METHOD A poster was created to remind the theatre team that a fresh tumour tissue and EDTA blood sample are the required samples to proceed with whole genome sequencing. Consent training is being provided to all clinical nurse specialists who look after CNS tumours in order to secure the record of discussion at any point in their hospital appointment. After several shadowing and doing the consenting process with supervision, they are expected to be able to be independent in doing this. RESULTS The clinical nurse specialist team have consented 24 out of 37 paediatric, teenage and young adult neuro- oncology patients that were operated since July 2022. Almost all of them have had their bloods taken intra- operatively with some taken whilst still an in-patient. CONCLUSIONS Whole genome sequencing will soon be offered to all neuro-oncology patients and the entire team will need to be equipped in securing the record of discussion in order for this test to proceed and this will be led by the clinical nurse specialist team.
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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 | 0.012 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.045 | 0.017 |
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".