Reply: Occipital bending in depression
Bibliographic record
Abstract
Sir, We thank Professor Siebert for this comment and interest in our recent article regarding occipital bending in depression. The focus of our article was to investigate the prevalence of occipital bending in this patient group relative to control subjects, and our findings suggest that this is separate to previous findings of asymmetry or cortical thinning in the occipital lobe (Peterson et al., 2009). We hypothesized that incomplete neural pruning may lead to the cranial space available for brain growth being restricted, or ventricular enlargement observed in depression (Kempton et al., 2011). This compresses the brain and causes torque and curvature around the other occipital lobe. The variation in skull base and vascular anatomy in this region is interesting in the context of our findings of the prevalence of occipital torque. Indeed, the more frequent right transverse sinus draining the superior sagittal sinus (Saiki et al., 2013) results in displacement of the interhemispheric cleft and occipital bending. Although rare, there are cases reported of depression being associated with irregularities in the transverse-sigmoid sinus or superior sagittal sinus anatomy (Katz et al., 2003; Nakagawa et al., 2012). The question therefore arises as to the precursor of this difference between our study groups in terms of skull base anatomy and vasculature. For example, it has been shown that significant relative enlargement of the supratentorial volume during development, relative to infratentorial volume, is accompanied by a rotation of the midline section of the tentorium towards the posterior cranial base (Erdoglija, 1989). Jeffrey (2002) also found that increases of supratentorial volume relative to infratentorial volume affect an inferoposterior rotation of the human foetal tentorium cerebelli. While anatomical variation of brain anatomy following vascular anatomical variation is accepted, it does not explain the increased incidence of occipital bending in depression that we have described.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.027 | 0.023 |
| Insufficient payload (model declined to judge) | 0.003 | 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".