Bibliographic record
Abstract
Detection and intervention in the very early phases of psychosis is possibly the field of schizophrenia’s best hope for substantive improvements in the outcome of schizophrenia spectrum disorders. Huge progress has been made in many mental health systems worldwide, offering specialized intervention programs for people experiencing first-episode psychosis (FEP).1 During the past decade a wide range of research and clinical work has explored the possibility of intervention before the onset of the full disorder.2 This work focuses on the pre-psychotic or prodromal stage of the illness, which has been defined as the period marked by changes from a person’s premorbid mental state and level of functioning up to the appearance of psychotic features.3 About 80% to 90% of people with schizophrenia describe various subthreshold symptoms in the months and years preceding psychosis, including changes in drive, perception, beliefs, attention, concentration, mood, affect, and behaviour.4 It is possible that much of the disability associated with schizophrenia develops during the prodromal period, in which poor social functioning and the beginning of negative symptoms form the foundation on which psychosis later develops. From retrospective studies,5 it seems as if most people with schizophrenia retrospectively report a prodromal period characterized by increasing problems in thinking, feeling, and behaving, but it is unclear how many people who may experience similar prodromal type symptoms will go on to develop a psychotic illness. Therefore, what is most important for early intervention would be to accurately identify people who may be at true risk of developing a psychotic illness. As the prodrome is essentially a retrospective concept, young people who are seen as being at risk for psychosis have been described as ultra-high risk or at clinical high risk because, as will be described in the first In Review paper in this issue,6 they are considered to be at high risk of developing psychosis based on clinical features. Dr Alison R Yung and Dr Barnaby Nelson,6 from the University of Melbourne, review the efforts of researchers in Australia, North America, and Europe to develop and test operational criteria for prospectively assessing the risk for psychosis and suggest that it is possible to reliably identify an at-risk mental state in which people have an increased risk—for example, 25% to 35%—of developing a diagnosable psychotic illness during several years.7 These young people are generally help-seeking adolescents or young adults, and have many other concerns. Comorbid diagnoses, in particular, anxiety, depression, and substance use disorders, are common. Impairment in social, role, and cognitive functioning, intermediate in severity to people with a psychotic illness and healthy control subjects, has been observed.8 It is these clinical concerns that form the basis for studies attempting to determine the predictors of psychosis. Results typically suggest that poor functioning, attenuated psychotic symptoms (such as unusual thought content and suspiciousness), substance misuse, and genetic high risk are among the common predictors.7,9 Some research groups have begun to study biological markers that may predict psychosis. There is growing awareness that measures of brain anatomy and function may represent vulnerability markers for increased psychosis risk.10–12 As described in the second In Review paper in this issue13 there has been an explosion of neuroimaging data. In their review, Dr Stephen J Wood and colleagues,13 from the University of Birmingham, put all the exciting new findings into perspective, both in terms of prediction of psychosis and where the area needs to go. The hypothalamic–pituitary–adrenal (HPA) axis functioning in people at clinical high risk has also been explored, using salivary cortisol measures to assess HPA axis integrity. Clinical high-risk people who eventually developed psychosis exhibited higher cortisol levels at several follow-up points prior to conversion, compared with those that did not.14 As our data has grown regarding the predictive validity of clinical high-risk criteria, and the range of impairments and difficulties these young people experience, research has moved from early identification to preemptive intervention. The goal of these studies reviewed by Yung and Nelson in this issue6 was to reduce symptoms and behavioural deficits associated with this stage of illness, and to prevent or delay the onset of the FEP. What next? There are several obvious steps. The first is that the field of psychosis needs to move toward improved prediction models, specifically adding biomarkers as to the clinical predictors. Such models, although they will ideally improve our understanding of psychosis, will require translation to make them clinically useful.2 The second is advancing our work in testing treatments for this population to have treatments that are specifically designed for them, and that may have some basis in established risk factors.2 Work on innovative treatments that not only address current concerns but also are preventive are much needed. Other important areas for the future is the growing concern that many young people who appear to be in the prodromal phase of a psychotic illness do not actually develop the illness but could not be described as healthy young people.8 This may be a concern that needs addressing in conjunction with recent advances in studying youth mental health and risk for mental health problems in general.15 Finally, as presented in the Yung and Nelson paper,6 there is a wealth of research exploring the existence of psychotic like experiences in the general population that has implications for this high-risk research as well as schizophrenia research.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".