Bibliographic record
Abstract
Conversion reactions can involve many neurologic functions ranging from movement and somatic sensation to the special senses and cognitive intellectual abilities. The diagnosis is made at the bedside and is usually straightforward. However, up to 15% of diagnosed conversion reactions subsequently prove to be due to missed neurologic conditions.1 The sooner new methods define the neurobiological mechanisms that underlie conversion reactions as well as subtle presentations of organic disease capable of mimicking them, the better for both patient and neurologist. The report in this issue of Neurology by Ghaffar et al. on noninvasive observations made by functional MRI (fMRI) in three patients with unilateral psychogenic sensory disturbance is a useful step in that direction.2 We also believe that further effort in this area can advance general understanding of how the nervous system works. The essential feature of a conversion reaction is that the complaints and findings do not follow known neurologic damage patterns.1 There is no recognized location within the motor, sensory, special sensory, or cognitive intellectual pathways where organic injury or malfunction will produce the reported symptoms or observed signs. The information that fMRI can obtain about what the nervous system is doing while …
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".