A novel tablet computer platform for advanced language mapping during awake craniotomy procedures
Bibliographic record
Abstract
A computerized platform has been developed to enhance behavioral testing during intraoperative language mapping in awake craniotomy procedures. The system is uniquely compatible with the environmental demands of both the operating room and preoperative functional MRI (fMRI), thus providing standardized testing toward improving spatial agreement between the 2 brain mapping techniques. Details of the platform architecture, its advantages over traditional testing methods, and its use for language mapping are described. Four illustrative cases demonstrate the efficacy of using the testing platform to administer sophisticated language paradigms, and the spatial agreement between intraoperative mapping and preoperative fMRI results. The testing platform substantially improved the ability of the surgeon to detect and characterize language deficits. Use of a written word generation task to assess language production helped confirm areas of speech apraxia and speech arrest that were inadequately characterized or missed with the use of traditional paradigms, respectively. Preoperative fMRI of the analogous writing task was also assistive, displaying excellent spatial agreement with intraoperative mapping in all 4 cases. Sole use of traditional testing paradigms can be limiting during awake craniotomy procedures. Comprehensive assessment of language function will require additional use of more sophisticated and ecologically valid testing paradigms. The platform presented here provides a means to do so.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".