NLM.WACM. A NEW LEARNING MODEL: A WALDORF ACTIVITY IN A CATALAN MUSEUM
Bibliographic record
Abstract
The amygdala (AG) is an almond-shaped heterogeneous structure located in the medial temporal lobe. The majority of previous structural Magnetic Resonance Imaging (MRI) volumetric methods for AG measurement have so far only been able to examine this region as a whole. In order to understand the role of the AG in different neuropsychiatric disorders, it is necessary to understand the functional role of its subnuclei. The main goal of the present study was to develop a reliable volumetric method to delineate major AG subnuclei groups using ultra-high resolution high field MRI. 38 healthy volunteers (15 males and 23 females, 21-60 years of age) without any history of medical or neuropsychiatric disorders were recruited for this study. Structural MRI datasets were acquired at 4.7 T Varian Inova MRI system using a fast spin echo (FSE) sequence. The AG was manually segmented into its five major anatomical subdivisions: lateral (La), basal (B), accessory basal (AB) nuclei, and cortical (Co) and centromedial (CeM) groups. Inter-(intra-) rater reliability of our novel volumetric method was assessed using intra-class correlation coefficient (ICC) and Dice's Kappa. Our results suggest that reliable measurements of the AG subnuclei can be obtained by image analysts with experience in AG anatomy. We provided a step-by-step segmentation protocol and reported absolute and relative volumes for the AG subnuclei. Our results showed that the basolateral (BLA) complex occupies seventy-eight percent of the total AG volume, while CeM and Co groups occupy twenty-two percent of the total AG volume. Finally, we observed no hemispheric effects and no gender differences in the total AG volume and the volumes of its subnuclei. Future applications of this method will help to understand the selective vulnerability of the AG subnuclei in neurological and psychiatric disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".