P.092 The effect of the timing of surgery on outcomes for incidental low-grade gliomas: a systematic review
Bibliographic record
Abstract
Background: Although previous research has suggested that patients with incidentally discovered low-grade gliomas (iLGG) who undergo surgery prior to the appearance of symptoms have improved outcomes compared to those who are symptomatic, an ideal approach to managing iLGG is not well-established. The purpose of this systematic review is to identify all cases of iLGG in the literature and characterize the effect of the timing of surgery on survival. Methods: We searched EMBASE, MEDLINE, and PubMed for articles related to iLGG. After duplicates were removed, the articles were then screened based on strict inclusion and exclusion criteria. Results: We retrieved 24/1377 unique articles with a total of 175 patients who underwent surgery for iLGG prior to symptoms appearing. The average age was 29.1yrs (range 1-62) and the mean follow-up period was 56 months (range 1-234months). Unfortunately, only 6/24 articles reported progression-free survival (average 32.4months) and only 1/24 reported 10-year survival. Conclusions: The articles we identified favored an early intervention for iLGG, however, there was a considerable lack of long-term follow-up and survival data to justify such a claim. Further studies need to be performed with adequate follow-up data in order to determine the optimal timing of surgical intervention for these patients.
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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 | 0.006 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".