Multidisciplinary Clinical Care in the Management of Patients Receiving Anti-GD2 Immunotherapy for High-Risk Neuroblastoma
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.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".