QOS-22IS A SEPARATE AFTERCARE CLINIC FOR PEDIATRIC CNS TUMOR PATIENTS NECESSARY?
Bibliographic record
Abstract
BACKGROUND: Over the past decades more and more patients diagnosed with a CNS tumor in childhood are long term survivors. Multiple publications have shown that long term morbidity is the highest in this disease group. METHODS: From the database of the primary nurse/ case manager data of all patients followed in the neurooncology aftercare clinic were collected. RESULTS: Over the past years around 120 patients with 260 visits were followed in this clinic on a yearly basis. PatienT's age was from 5 to 50 years, visit frequency from 4 times to once a year. The clinic was held once a week in the afternoon with 4 to 6 patients per clinic. At the start of each clinic the most recent imaging was reviewed by the pediatric neuroradiologist and the ongoing problems and clinical findings were discussed. Patients were initially seen by the most relevant discipline (neurosurgery, neurooncology or radiation oncology) and then seen by the whole group. All were also seen by the rehabilitation team (occupational therapy, physiotherapy and dietician) as well as by the social worker and the neuropsychologist. If other consults like ophthalmology or imaging are scheduled for the visit, they are organized for the morning. CONCLUSION: From our experience a separate aftercare clinic for pediatric CNS tumor patients is necessary given the multiple morbidities and issues. Patients and parents appreciate this aftercare model with a multidisciplinary team and multiple appointments in one day to address all their issues.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".