QOS-21A MODEL OF CARE FOR PEDIATRIC CNS TUMOR PATIENTS
Bibliographic record
Abstract
BACKGROUND: CNS tumors are the most common solid tumors in childhood. Given the complexity and comorbidities multiple health care workers from different disciplines are involved in the care. METHOD: Since 2008 a multidisciplinary team under the leadership of the neurooncologist for the care of pediatric CNS tumor patients was futher developed and prospectivly more disciplines were added due to the needs of the patients. RESULTS: Since 2008 around 24 patients with a new diagnosis of a pediatric CNS tumor and 5 relapses/ progression were treated on a yearly basis. Patients initally seen in the emergency room were visited by the neurosurgeon and neurooncologists following the initial imaging. Immediate inpatient care around the time of surgery was shared between these two disciplines. Disclosure was performed by the neurosurgeon and neurooncologist together including a dedicated social worker, the radiation oncologist was also present in case radiation therapy was needed. All these patients have had the same primary nurse/ case manager taking care of them since diagnosis. Given the different aspects of care the following disciplines were also part of the team: neuroradiology, neuropsychology, endocrinology, neuroophthalmology, social work, child life, occupational therapy, physiotherapy, dietician, interlink nurse and clinical research associate - all disciplines with a dedicated professional. Patients and parents do appreciate this model of care. CONCLUSION: Pediatric CNS tumor patients require a multidisciplinary care team with a dedicated case manager and a dedicated leader for optimal care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".