NURS-03. DEVELOPMENT OF A NEURO-ONCOLOGY LONG TERM FOLLOW UP NURSE CLINICIAN AT A TERTIARY HEALTH CARE CENTER
Bibliographic record
Abstract
Brain tumours are the second most common malignancy in childhood. With advances in treatment overall survival is around 70% increasing to 80-90% for low grade tumours. Many of these survivors have significant long term health risks and late effects from therapy; including neurocognitive deficits, endocrine dysfunction and hearing loss adding unique challenges in providing follow up and education to prepare the survivor for transition. At our center we currently follow approximately 90 brain tumour survivors in our long term follow up (LTFU) clinic, as more children survive their disease this number will only increase with time. Historically these patients have been seen in a multidisciplinary clinic with the Neuro-Oncology nurse clinician (NONC) as the point person for the families. The NONC was responsible for all on treatment patients, palliative care, patients off treatment for less than two years and long term survivors. It was well recognised that the LTFU patients were not being adequately cared for given the acuity and high needs of the other patients. With the development of the NOLTFNC brain tumour survivors now have a dedicated clinician who is responsible for providing support, coordinating care, educating survivors and families about late effects and transitioning patients. The NOLTFNC has been in place for 18 months and we plan to evaluate the impact of this role on survivors and their families by asking them to fill out a questionnaire. The goal being to improve services as we move forward in providing LTFU care and preparing survivors for transition.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.192 | 0.072 |
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".