Occipital bending in depression
Bibliographic record
Abstract
There are reports of differences in occipital lobe asymmetry within psychiatric populations when compared with healthy control subjects. Anecdotal evidence and enlarged lateral ventricles suggests that there may also be a different pattern of curvature whereby one occipital lobe wraps around the other, termed 'occipital bending'. We investigated the prevalence of occipital bending in 51 patients with major depressive disorder (males mean age = 41.96 ± 14.00 years, females mean age = 40.71 ± 12.41 years) and 48 age- and sex-matched healthy control subjects (males mean age = 40.29 ± 10.23 years, females mean age = 42.47 ± 14.25 years) and found the prevalence to be three times higher among patients with major depressive disorder (18/51, 35.3%) when compared with control subjects (6/48, 12.5%). The results suggest that occipital bending is more common among patients with major depressive disorder than healthy subjects, and that occipital asymmetry and occipital bending are separate phenomena. Incomplete neural pruning may lead to the cranial space available for brain growth being restricted, or ventricular enlargement may exacerbate the natural occipital curvature patterns, subsequently causing the brain to become squashed and forced to 'wrap' around the other occipital lobe. Although the clinical implications of these results are unclear, they provide an impetus for further research into the relevance of occipital bending in major depression disorder.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".