A systematic study of molecular diagnosis, treatment, and prognosis in infant-type hemispheric glioma: An individual patient data meta-analysis of 164 patients
Bibliographic record
Abstract
BACKGROUND: Due to the novelty and rarity of infant-type hemispheric glioma (IHG), optimal treatment and factors determining clinical outcomes are yet to be established. METHODS: We curated a series of 164 patients with IHG; 155 identified by methodical literature search and nine additional patients contributed by collaborators. RESULTS: All tumors were hemispheric, diagnosed at a median age of 3.4 (0-52) months, and frequently (95%) non-metastatic. One hundred forty-two (86.5%) tumors harbored fusions involving receptor tyrosine kinase (RTK) genes (ALK [67/142, 47%], NTRK1/2/3 [32/142, 22.5%], ROS1 [29/142, 20.4%], MET [13/142, 9.2%], and ABL2 [1/142, 0.7%]). Sixty-four percentage, 20%, and 8% of patients were treated with surgery and adjuvant chemotherapy, surgery-only, and surgery plus targeted therapy, respectively. Five patients received radiation. Three-year event-free survival (EFS) and overall survival (OS) was 49.5% [40.7-60.2] and 79.6% [72.1-87.9], respectively. Twenty-two patients succumbed to disease, of which tumor progression (8/22, 36%) and intra-cranial hemorrhage (5/22, 23%) were the most common causes. Multivariate analysis showed that the factors most associated with an increased risk of death were no treatment except for surgery and presence of residual tumor after definitive surgery. These findings present a challenging dichotomy where surgery is both a serious risk factor for early death and, when successful, a benefit. CONCLUSIONS: Together, these findings show that IHG is a fusion driven tumor of the very young that is survivable even after progression. While optimal primary therapy for patients with IHG has yet to be established, the findings of this meta-analysis suggest treatment should focus on lowering surgical morbidity and improving its success.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.021 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".