Does dominant pedunculopontine nucleus exist?
Bibliographic record
Abstract
Sir, We read with great interest the paper by Fling et al. (2013) recently published in Brain. In this imaging study, patients with Parkinson’s disease with freezing of gait (FOG), without FOG, and age-matched controls were studied with diffusion tensor imaging to identify group and inter-hemispheric differences of tract quality and quantity in the pedunculopontine nucleus (PPN) network. Interestingly, patients with Parkinson’s disease with FOG had significantly less right PPN tract volume in terms of absolute values and ratio with the left side (i.e. greater laterality index) compared to patients without FOG. Furthermore, solely in the Parkinson’s disease with FOG group, linear regression analysis significantly correlated greater lateralization of PPN tract volume with poorer performance in action inhibition tasks. The authors concluded that right hemispheric circuitry might be uniquely involved in the pathophysiology of FOG (Fling et al., 2013). In keeping with these findings, clinical evidence from the DATATOP cohort supports the notion that patients with left-onset Parkinson’s disease have a moderately increased risk for future development of FOG (Giladi et al., 2001). Moreover, recent studies have pointed to the right hemispheric network involved in visuospatial navigation as an important determinant in the pathophysiology of FOG (Cremers et al., 2012; Peterson et al., 2014).
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.012 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".