Surgical Pitfalls, their Consequences, Transient Complications
Bibliographic record
Abstract
The methodology of this paper is based entirely on the experiential backgrounds of the authors. It outlines those factors which have become recognized as potentially important issues to patients who are considering recommendations of surgical treatment for their intractable epilepsy. Thus, on the one hand, it includes the important generic aspects of Informed Consent, while on the other hand there must be a very comprehensive and, when the operation is to be carried out under local anesthesia, a very detailed explanation of the preparation and the sequential steps in the surgical procedure. This should also entail a brief description of the roles of the various "team" members during the operative procedure. There are well-recognized complications associated with the various surgical procedures for the treatment of epilepsy. Further, there are predictable deficits following some of these procedures, some of which might be permanent and some of which may be transient. These pitfalls are briefly discussed.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".