LMI-01DEVELOPMENT OF CLINICAL GUIDELINES FOR THE TREATMENT OF LOW GRADE GLIOMAS IN LOWER AND LOWER MIDDLE INCOME COUNTRIES – A SIOP PODC INITIATIVE
Bibliographic record
Abstract
The treatment of children with low grade gliomas remains a major challenge with many different treatment options available. In lower and lower middle income countries (LMIC) clinical trials are seldom available, and there are limitations in which treatments can be offered. The International Society of Paediatric Oncology (SIOP) has a committee named Paediatric Oncology in Developing Countries (PODC) which has a number of working groups. The SIOP PODC Adapted Treatment Regimens Working Group produces recommendations for the management of childhood cancers in LMIC, and guidelines for their implementation as well as for continuous quality improvement based on local outcome data. A group of clinicians from various specialities who treat children with CNS tumours in LMIC from around the globe, as well as a number of clinicians from high income countries (HIC) with experience of LMIC, was convened and met monthly via the C4K teleconference facility. Evidence applicable to all aspects of care including diagnosis, surgery, radiotherapy, chemotherapy, relapse treatment, late effects and follow up was discussed and a consensus was reached. The guidelines were stratified according to the facilities and personnel available via SIOP PODC standardised service levels for the care of patients with low grade gliomas. The recommendations will be circulated widely and discussed at a SIOP PODC meeting. They will then be ratified by the SIOP Scientific Committee prior to submission for publication. Guidelines such as these provide valuable support and guidance for those working in LMIC, and may be a valuable contribution to standardisation of care.
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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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.022 | 0.018 |
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".