Towards a Risk-Based Follow-Up Surveillance Imaging Schedule for Children and Adolescents with Low-Grade Glioma
Bibliographic record
Abstract
The frequency and duration of imaging surveillance in children and adolescents with pediatric low-grade gliomas (pLGGs) aims for the early detection of recurrence or progression. Although surveillance of pLGGs is performed routinely, it is not yet standardized. The aim of the current review is to provide a comprehensive synthesis of published studies regarding the optimal frequency, intervals, and duration of surveillance. Several key influencing factors were identified (age, the extent of resection, the tumor location, the histological type, and specific molecular characteristics). However, the lack of consistent definitions of recurrence/progression and the extent of resection meant that it was not possible to perform a meta-analysis of the data from the 18 included articles. This review highlights the need for updating the definition of these terms for uniform and global use both in routine clinical practice as well as in upcoming trials. Thus, future studies on the heterogenous group of pLGGs will allow for the better tailoring of both the frequency and duration of imaging surveillance protocols in relevant settings.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".