Comparison of the reliability of brain lesion localization when using traditional and stereotactic image-guided techniques: a prospective study
Bibliographic record
Abstract
OBJECT: Accurate localization of brain lesions is of utmost importance. Traditional methods of localization that involve the use of neuroimaging and surface anatomy have been replaced in certain cases by using frameless stereotactic neuronavigational systems. Even though these systems have been found to be accurate, no studies have been conducted to investigate whether the systems provide improved localization accuracy compared with traditional methods. METHODS: Twenty-two patients undergoing image-guided surgery with the aid of the Stealth Neuro-Station were prospectively enrolled in this study. All patients underwent standard magnetic resonance or computerized tomography imaging, as well as special Stealth-sequenced imaging acquired using scalp fiducial markers. Traditional and Stealth estimates of the surface projection of lesions were determined, digitally photographed, and later compared. The mean (+/- standard deviation) error associated with traditional localization of lesions was 1.1 +/- 0.7 cm in the mediolateral plane and 1.3 +/- 1.1 cm in the anteroposterior plane. This error was not significantly affected by the size or location of the lesion. CONCLUSIONS: Findings of this study indicate that the conventional localization technique used to demarcate brain cortical and subcortical lesions has an error of approximately 1 to 1.5 cm in both the mediolateral and anteroposterior directions. This error can be reduced by judicious use of image-guided techniques.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".