Bibliographic record
Abstract
To the Editor: Re: False-negative transcranial motor-evoked potentials during scoliosis surgery causing paralysis. Spine 2009; 34:e896–900. We read with concern the report by Modi et al1 in a recent issue of Spine detailing their unfortunate experience, with a patient awaking paraplegic after spine deformity correction surgery, with apparently no change in the evoked responses. This would appear to be, as they state, the first such case in the literature and as such is important. In this case, the surgeons used an automated neuromonitoring system that relied on MEPs only. This type of approach is new and controversial. Recently, Hsu et al proposed, in this journal, the MEP and EMG only approach for monitoring of spine deformity surgery.2 At the time, we expressed concern about this approach3 and the potential for sensory deficits to be missed. The relative ease, safety, and efficacy of SSEPs have been well-established and their use is, in our opinion, a required component of neuromonitoring in spine deformity surgery. In this case, we are provided with representative traces from 4 time points during the case, however, these are somewhat difficult to read given overlap between traces. It is apparent from these traces that there were some changes in the recordings during the surgery. It is of course, unknown whether these changes would have been detected using SSEPs, but that possibility must be considered. Also of grave concern is the possibility that this was a real change in MEPs that was not detected using the automated system. Reliance on automation in this field is a real concern among many professionals in the field. For many of us it is not clear that these systems are as safe as the neurophysiologist driven systems. Head-to-head trials of the 2 types of system are only just starting. We would support the call for more basic and applied research in this field. Jonathan Norton University of Alberta Hospital Alberta, Canada
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.125 | 0.067 |
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".