Bibliographic record
Abstract
Why is it that naming skills decline in so many patients after left temporal lobe resection for epilepsy? More importantly, why do naming skills decline even when language mapping allows surgeons to spare cortical zones identified as critical for naming? On average, patients who undergo left temporal lobe resection face a 30% to 50% risk of significant postoperative decline in naming, whether or not language mapping is employed.1,2 Over the last decade, Hamberger and colleagues' impressive studies have contributed to our understanding of temporal lobe organization of language, cognitive effects of epilepsy surgery, and optimum methods for language mapping in surgical patients. Their primary line of inquiry involves auditory naming, assessed by having patients provide the word corresponding to a verbal description (“The yellow part of an egg”). In contrast, visual naming involves providing the name for a pictured object. The idea is that auditory naming is a closer analog than visual naming to the word-finding problems that patients encounter in everyday life. These authors found that auditory naming tends to cluster anteriorly in the left temporal lobe. This should render auditory naming more vulnerable to temporal lobectomy than visual naming, which tends to be located more …
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.013 | 0.024 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".