A multi-disciplinary consensus statement concerning surgical approaches to low-grade, high-grade astrocytomas and diffuse intrinsic pontine gliomas in childhood (CPN Paris 2011) using the Delphi method
Bibliographic record
Abstract
Astrocytic tumors account for 42% of childhood brain tumors, arising in all anatomical regions and associated with neurofibromatosis type 1 (NF1) in 15%. Anatomical site determines the degree and risk of resectability; the more complete resection, the better the survival rates. New biological markers and modern radiotherapy techniques are altering the risk assessments of clinical decisions for tumor resection and biopsy. The increasingly distinct pediatric neuro-oncology multidisciplinary team (PNMDT) is developing a distinct evidence base. A multidisciplinary consensus conference on pediatric neurosurgery was held in February 2011, where 92 invited participants reviewed evidence for clinical management of hypothalamic chiasmatic glioma (HCLGG), diffuse intrinsic pontine glioma (DIPG), and high-grade glioma (HGG). Twenty-seven statements were drafted and subjected to online Delphi consensus voting by participants, seeking >70% agreement from >60% of respondents; where <70% consensus occurred, the statement was modified and resubmitted for voting. Twenty-seven statements meeting consensus criteria are reported. For HCLGG, statements describing overall therapeutic purpose and indications for biopsy, observation, or treatment aimed at limiting the risk of visual damage and the need for on-going clinical trials were made. Primary surgical resection was not recommended. For DIPG, biopsy was recommended to ascertain biological characteristics to enhance understanding and targeting of treatments, especially in clinical trials. For HGG, biopsy is essential, the World Health Organization classification was recommended; selection of surgical strategy to achieve gross total resection in a single or multistep process should be discussed with the PNMDT and integrated with trials based drug strategies for adjuvant therapies.
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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 | 0.201 | 0.157 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".