Multidisciplinary Clinical Care in the Management of Patients Receiving Anti-GD2 Immunotherapy for High-Risk Neuroblastoma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The addition of anti-disialoganglioside-2 (GD2) monoclonal antibodies (mAbs) such as dinutuximab and naxitamab to standard therapies for high-risk (HR) neuroblastoma has significantly improved outcomes for children with this devastating disease. The care for these young patients receiving treatment for HR neuroblastoma is complex, with need for the involvement of a multidisciplinary team. Clinical implementation of anti-GD2 mAb treatment requires the same harmonized team approach. The authors share the development process of this coordinated team method and practical recommendations for administration of anti-GD2 mAbs and adverse event (AE) management. Successful collaboration between nurses and other team members ensures optimal treatment and comfort of patients and their families. The primary focus of this approach is to mitigate and manage AEs associated with anti-GD2 mAb treatments, such as pain, hypotension, allergic reactions, and hypertension, and to ensure safe and effective use of anti-GD2 mAbs. The two treatments approved for use in patients with neuroblastoma, dinutuximab for patients with HR disease following a partial response or better to frontline multimodal therapy and naxitamab for refractory or relapsed HR disease in the bone or bone marrow, were studied in different administration settings and follow different regimens and infusion schedules. Therefore, AE management requirements are specific to each treatment. The awareness of these differences and implementation of appropriate AE management strategies in clinical practice are important to ensure the best possible outcomes for patients with HR neuroblastoma.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it