Longitudinal Single Photon Emission Computed Tomography Neuroimaging as an Indication of Improvement in Psychiatric Disorders in a Community Psychiatric Practice
Bibliographic record
Abstract
In the community, there is a need to more objectively evaluate the response of common chronic psychiatric disorders to treatment. Brain single photon emission computed tomography (SPECT) indirectly measures cerebral functional activity by uptake of a radiotracer, which follows regional cerebral blood flow. Brain 3D Thresholded SPECT scans are thresholded three dimensional images derived from brain SPECT data. A retrospective community study of longitudinal (before and after treatment) brain 3D Thresholded SPECT scans of 73 patients with all-cause psychiatric disorders (most frequent diagnostic clusters: attention-deficit hyperactivity disorder, post-mild traumatic brain injury, affective disorders, psychotic disorders, post-viral chronic syndromes), shows these baseline SPECT scans predict improvement (non-worsening to large improvement) in clinical functioning with a sensitivity of 94% (95% confidence interval 86-98%) and a specificity of 67% (95% confidence interval 21-94%). In contrast, contemporaneous analysis by the same radiologist of conventional 2D reading of the same before and after treatment brain SPECT scan data of the same 73 patients, predicted improvement (non-worsening to large improvement) in clinical functioning with a sensitivity of only 26% (95% confidence interval 17-37%) although with a specificity of 100% (95% confidence interval 44-100%). These data suggest 3D Thresholded SPECT scans can provide the clinician with a more objective measure for verifying improvement in psychiatric disorders seen in the community, consistent with prior studies of SPECT as a measure of neurobiological change. Furthermore, these data suggest 3D Thresholded SPECT scans may have clinical application in guiding treatment and potentially improving outcomes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".