Bibliographic record
Abstract
Abstract Over the last two decades, molecular genetics and brain imaging have guided efforts to find the causes of schizophrenia. It is becoming increasingly clear that many genes are involved in schizophrenia and that they interact with other factors in very complex ways, which have not yet been elucidated. Neuroimaging techniques have allowed scientists and physicians to examine brain structure, function, and chemistry in living patients with schizophrenia but results so far have been disappointing. No two patients seem to share exactly the same combination of clinical symptoms or physical findings. Yet all have the syndrome recognized as schizophrenia. The author of this accessible, well-written book argues that it is time to set aside the search for a single cause of schizophrenia and focus on the disease’s final common pathway. He highlights clues from a wide range of research, including neurotransmitter, psychophysiological, and brain imaging studies. He then describes possibilities for the final common pathway at an understandable level in the context of what is already known about schizophrenia. While there are no preferred models of schizophrenia, a pattern is emerging which implicates those structures in the brain known to be important in integrating perception, cognition, and affect. A better understanding of these processes will be critical for developing mor effective treatments. This book will help advance that effort. It will be of great value to psychiatrists, psychologists, neurologists, neuroimagers, and basic scientists working in the field of shizophrenia research, and to their students and trainees. It will also be of interest to cliniciansand scientists concerned with other neuropsychiatric disorders, and to the families of those diagnosed with schizophrenia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".