Bibliographic record
Abstract
Computer-based brain-imaging technologies were introduced into medicine beginning in the 1970s, with the introduction of computed axial tomography (CAT or CT) and, subsequently, magnetic resonance imaging (MRI), magnetic resonance spectroscopy, functional MRI, positron emission tomography (PET), and single photon emission computed tomography (SPECT). These technologies have revolutionized the practice of neurology but, to date, have had less impact on the practice of psychiatry. Nevertheless, brain imaging has become an increasingly important area of psychiatric research over the past 30 years and is beginning to influence how we understand psychiatric illness and how it should be treated. Brain imaging arrived on psychiatry's doorstep when explanatory models for psychiatric illness were in transition. The introduction of the first generation of psychotropic medications challenged the field to address biological determinants of mental illness, as did the emergence of new research establishing that many major mental illnesses have a high degree of heritability. CT and MRI captured the imagination of the field by their ability to visually present the 3-dimensional structure of the brain-as if this might settle once and for all whether illnesses such as schizophrenia are really brain diseases. The search for the structural basis of schizophrenia offers a useful example of how brain imaging informs our understanding of the illnesses we psychiatrists treat. That CT scanning revealed chronically ill patients with schizophrenia to have enlarged lateral ventricles1 brought to the fore the need to understand the disorder's underlying neurobiology. Subsequent studies have led us to understand that structural brain changes are anything but simple; they point to a model of dis ease that should cause us to challenge many of our assumptions about schizophrenia. Although the magnitude of the ventricular enlargement and subsequently described gray matter volume deficits in schizophrenia2 had a medium effect size, further studies revealed that these differences were greatest in patients who had been ill for longer periods and in those who were more severely affected.3 The extent to which illness duration, compared with illness severity, contributes to the magnitude of structural brain abnormalities observed remains an important topic of debate, as these factors are often confounded. Several longitudinal studies have provided evidence for progressive brain changes in patients with schizophrenia,4,5 reinforcing the Kraepelinian conceptualization of schizophrenia as a progressive, deteriorating illness. However, other studies have failed to find that brain abnormalities significantly progressed over time,6,7 which should cause us to ask in what ways schizophrenia should be understood as a progressive illness. Longitudinal studies of patients presenting with a first episode of schizophrenia provide clear support for ongoing clinical improvement over time, rather than worsening of symptoms, cognition, and community functioning.8,9 Relapses, which are all too frequent in schizophrenia, are more determined by medication nonadherence than by the natural history of the illness. The magnitude of structural brain abnormalities and the degree to which they change over time may to some degree reflect risk but are likely to be only minor determinants of the variance in outcome. That some of the brain differences observed might be related to medication exposure, nutrition, endocrine changes, or differences in activity levels cannot yet be excluded.10 Thus, although some might assume that identifying structural brain abnormalities in patients with schizophrenia could contribute to our pessimism about treatment outcomes, the very modest magnitude of these findings, the overwhelming degree of overlap with the normal population, and the questionable progression of these changes over time should cause us to question this interpretation. How structural brain abnormalities relate to the biology of other major mental disorders such as depression, bipolar disorder, posttraumatic stress disorder, and anorexia nervosa has also been a major focus of psychiatric research. …
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".