Neuropsychological differentiation of children and adults with and without non-psychotic, unipolar major depressive disorder.
Bibliographic record
Abstract
The current investigation was conducted to see if it would be possible to differentiate groups of participants with and without depression based on data from a comprehensive neuropsychological assessment. The first goal was to apply cluster analytic algorithms to the neuropsychological data for participants with and without depression. This was conducted in separate procedures for the child and adult groups. The second goal was to examine the internal validity of the groups, using multiple clustering algorithms. The third goal was to examine if internally valid solutions represented groups or subgroups that were comprised by a large majority of participants with a diagnosis of depression. Participants in the study were two hundred and ninety-four clients referred by neurologists in the greater Indianapolis, Indiana area, to an independent clinic for neuropsychological evaluation. Cluster analyses utilizing neuropsychological data yielded reliable and technically valid cluster solutions for both the child and adult data. The child and adult cluster solutions were characterized by performance level across most subtests of the neuropsychological battery, with a greater division of performance levels present in the adult cluster solutions. Both the child and adult solutions were comprised of clusters that did not differ significantly with respect to a diagnosis of depression. Therefore, the technically valid cluster analyses utilized in the current investigation, conducted on data from a comprehensive neuropsychological evaluation, did not differentiate, in a statistically significant way, participants with and without a diagnosis of depression in both the child and adult groups. Additional cluster analyses conducted with only sensorimotor data identified cluster solutions that were characterized by unique patterns of finger tapping performance and grip strength. Both child and adult solutions in the additional analyses were not comprised of clusters that differed significantly regarding a diagnosis of depression. Thus, the technically valid solutions from the additional analyses in the current investigation, conducted on sensorimotor data, also did not differentiate, in a statistically significant way, participants with and without a diagnosis of depression in both the child and adult groups. The implications, limitations, and future research considerations arising from the current investigation were discussed.Dept. of Psychology. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .A36. Source: Dissertation Abstracts International, Volume: 66-02, Section: B, page: 1159. Adviser: Byron Rourke. Thesis (Ph.D.)--University of Windsor (Canada), 2004.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".