Image-guided resection of high-grade glioma: patient selection factors and outcome
Bibliographic record
Abstract
OBJECT: In patients with glioma, image-guided surgery helps to define the radiographic limits of the tumor to maximize safety and the extent of resection while minimizing damage to eloquent brain tissue. The authors hypothesize that image-guided resection (IGR) techniques are associated with improved outcomes in patients with malignant glioma. METHODS: Data recorded in 486 patients enrolled in the Glioma Outcomes Project were analyzed in this study. Demographic data and outcomes in patients who underwent IGR were compared with those in patients who underwent resection without IGR. Univariate analysis performed with chi-square testing was used to compare patient presentation, tumor characteristics, and death rates. Multivariate logistic regression was used to predict various outcome parameters. Patients who underwent IGR were younger and had smaller, lower-grade tumors than those in whom IGR was not performed. They were more likely to present with seizure and normal consciousness. Unexpectedly, gross-total resection was performed in significantly fewer patients with IGR than in individuals without IGR. Patients with IGR were more likely to be discharged home with the ability to live independently, and they had a shorter duration of hospital stay than patients without IGR. Survival was significantly longer in patients who underwent IGR, but multivariate analysis showed that glioblastoma multiforme (GBM) and age accounted for these observations. CONCLUSIONS: Selection bias occurs regarding patients who receive IGR; these biases include younger age, presentation with seizure and normal level of consciousness, tumor diameter less than 4 cm, and non-GBM on histopathological studies. Outcome appears to be improved in patients who undergo IGRs of high-grade gliomas. It is unclear if these improved outcomes are due to the selection of a more favorable patient population or to the IGR techniques themselves. It is likely that the full potential of image guidance in glioma surgery will not be realized until it is applied to a wider range of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".