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The promises and challenges of early intervention in psychotic disorders

2008· article· en· W1942388286 on OpenAlexaff
Ashok Malla

Bibliographic record

VenueWorld Psychiatry · 2008
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsMcGill University
Fundersnot available
KeywordsIntervention (counseling)PsychiatrySchizophrenia (object-oriented programming)PsychosisPsychologyPsychotherapistMedicineMental health

Abstract

fetched live from OpenAlex

A burgeoning interest in understanding and treating the early phase of psychotic disorders, especially schizophrenia, has brought forth a sense of optimism of altering the course of these disorders. McGorry et al highlight many aspects of the progress made, as well as some of the challenges to furthering the application of a broader preventive model of care based on a hierarchical model of understanding mental disorders. It may not be entirely ironic that development of early intervention theory and practice in psychiatric disorders should have started with the disorder viewed most pessimistically with poor outcome (schizophrenia). Indeed, a great deal of progress has been made since the initial seminal studies of first episode psychosis 1 and the influential review by Wyatt 2. Such progress has extended beyond understanding the effects of delay in treatment to a more substantial understanding of neurobiology and outcome during early phase of psychotic disorders. It has been particularly remarkable that, while research in phenomenology, neurobiology and cognitive psychology of first episode psychosis and the putative periods preceding the onset of psychosis has flourished, there has been a parallel and equally prolific development of services specializing in treatment of early phases of the illness. Such developments have taken research out of artificial settings to real life new specialized services, thus making available large epidemiologically based cohorts of subjects for investigation. Such research is likely to be more meaningful in the long run, as the findings will be applicable to larger groups of patients. As McGorry et al suggest, it is time now to think more broadly and extend the scope of such developments in service and research to a larger group of disorders without the constraint of a strictly categorical diagnostic system. Despite the well justified enthusiasm, there are, however, a number of issues that remain either unclear or unaddressed. The term “early intervention” has often been taken to imply “earlier” intervention predicated on an association between duration of untreated psychosis (DUP) and clinical outcome. However, this is an oversimplification: there is in fact much more to “early intervention” than simply intervening early 3. The evidence to support enriched and comprehensive interventions is indeed strong and replicated in controlled studies 4–6 and confirmed in a recent meta-analysis 7. While it requires no more than face validity to support quick, unencumbered and user-friendly access to specialized treatment of new cases of psychotic disorders, the evidence for more elaborate and relatively expensive interventions to improve early case detection remains either confined to specific jurisdictions 8 or applicable only to a subgroup of patients 9. In order to benefit larger number of patients, it may be easier to convince mental health policy makers to apply a more effective treatment model with improved access than to expect them to support elaborate and expensive interventions to reduce DUP through active case detection. There is still a need to identify what methods of early case identification and improved access would work in which settings, given large variations in composition of populations (e.g. ethnicity, urban vs. rural setting) and nature and quality of the prevailing primary and specialist health care. On the other hand, large scale campaigns at the community level to improve general mental health literacy and engage communities in a dialogue about mental illness have heuristic value even if their direct impact on reducing delay in treatment of specific disorders may be difficult to demonstrate. McGorry et al correctly identify the greater conceptual accuracy of “ultra-high risk” as opposed to “prodromal” patients to whom interventions could be provided to prevent or delay onset of psychosis. While there has been progress in demonstrating efficacy of individual interventions in small controlled trials, we are not yet at a stage to recommend any particular approach. Apart from the need for more substantial evidence, there are several reasons for such caution. The transition from a non-psychotic high risk state to psychosis occurs in only a fraction of such patients, even without the use of antipsychotic medications, especially if they are provided with adequate care and support for the problems they present with. This raises the risk of treating many more false positives for a putative impending psychosis. Further, not enough attention has been paid to the relatively fluid and ambiguous boundary between sub-threshold and threshold level of symptoms of psychosis, creating a risk of reporting results based on a categorical fallacy. Until such time as further methodologically sound research using large samples produces clear evidence based interventions, we run the risk of encouraging clinicians to become cavalier in using antipsychotic medications for treating symptoms they observe over a single assessment, as is already happening in many jurisdictions. Other major challenges that must be faced, if “early intervention” is to benefit a larger population of patients, include the patients' refusal to accept or engage in treatment (estimate 15–50%), those who drop out early in the course of or do not adhere to treatment, and those who present with substance abuse as an additional problem. Lack of adherence to treatment and presence of substance abuse have been identified as major obstacles to achieving and maintaining symptomatic remission following treatment of first episode psychosis 10–12. Indeed, such malleable predictors of outcome overshadow the significance of delay in treatment in achieving better outcomes. Further, it appears that the gains made with specialized treatment of early phase of psychosis over the first two years are difficult to sustain 5, and further systematic study of the length of specialized treatment is required if we are to make a difference in the longterm course of psychotic disorders. Last, but not least, there is a dire need to understand the process of recovery and what promotes or hinders it during the early “critical period”. Both qualitative and quantitative research, which takes into account patients' and families' perspectives and examines the effect of various treatments on recovery 13, should be a priority for the early intervention field.

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 imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.048
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0020.006
Scholarly communication0.0030.009
Open science0.0020.006
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0080.001

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.

Opus teacher head0.028
GPT teacher head0.298
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

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Citations7
Published2008
Admission routes1
Has abstractyes

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