MétaCan
Menu
Back to cohort
Record W7024833668

Towards a better understanding of primary negative symptoms: A longitudinal study in first-episode psychosis

2016· dissertation· en· W7024833668 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2016
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicModel Reduction and Neural Networks
Canadian institutionsnot available
FundersCanadian Institutes of Health ResearchCanada Research ChairsPfizerAstraZenecaEli Lilly and Company
KeywordsLongitudinal studyPsychosisLongitudinal dataPrimary care
DOInot available

Abstract

fetched live from OpenAlex

Background: Schizophrenia is characterized by positive (hallucinations, delusions) and negative (blunted affect, avolition) symptoms.Negative symptoms can be classified as either primary (central to the illness) or secondary (induced by positive symptoms, depression, or extrapyramidal symptoms, for example).Primary negative symptoms have been more consistently and robustly related to a worse functional outcome and still represent an unmet therapeutic need.This project set out to increase our understanding of these core symptoms by: 1) exploring the proportion of primary and secondary negative symptoms among those who did not remit; 2) examining medication adherence and clinical insight (awareness of mental illness, belief in response to medication, and belief in need for treatment) in relation to primary negative symptoms; and 3) confirming previous neuroimaging markers of remission and exploring for markers of primary negative symptoms.Participants and setting: The final sample included 385 first-episode of psychosis (275 diagnosed with schizophrenia or a related spectrum disorder) clients treated from January 2003 through April 2015 at the Prevention and Early Intervention Program for Psychoses at the Douglas Mental Health University Institute in Montreal, Canada.For the neuroimaging data, there were 101 first-episode of schizophrenia clients who completed a baseline MRI scan, of which, 75 completed a 1-year follow-up scan.Main outcome measures: Remission was defined as achieving a global rating of mild or less on eight core symptoms (four positive and four negative) and maintained for six months (Andreasen et al. (2005) Am J Psychiatry, 162, 441-449).Primary negative symptoms (PNS) was defined as a global rating of moderate or worse severity on one negative symptom sustained for six months in the absence of clinically relevant positive, depressive, and extrapyramidal symptoms (Hovington et al. (2012) BMC Psychiatry, 12, 1-11).Ratings were based on the Scale for the Assessment of Negative Symptoms (Andreasen, N.C.(1984) Iowa City, University of Iowa) and the Scale for the Assessment of Positive Symptoms (Andreasen, N.C.(1984) Iowa City, University of Iowa).iv Results: After one year of treatment, 20% of clients were considered to be full remission.Among the Non-Remitted clients, 40% presented with PNS and 28% with secondary negative symptoms (2nd-NS).Similar proportions were found after two years of treatment and 1-year outcome significantly predicted 2-year outcome.Clients with PNS and 2nd-NS displayed poorer insight on all three insight variables across the first year of treatment compared to all other clients.Intriguingly, insight did not alter as a function of medication adherence among the PNS clients, but did among the other clients with a significant effect observed for 'belief in the need for treatment'.Finally, smaller hippocampal tail and parahippocampal cortex (PHC) volumes were verified as markers of not achieving remission.Now, compared to the other Non-Remitted clients, those with PNS had a significantly smaller PHC volume but did not differ in hippocampal tail volume.Moreover, there was a significant decrease in right PHC volume in the PNS clients over the one year follow-up period with a trend-level decrease in the left PHC.Conclusions: A large proportion of unremitted clients presented with PNS.In contrast to much of the current literature, clients with PNS do appear amenable to treatment; however, current treatments for PNS are rather inadequate and newer, more efficacious treatments are needed.A smaller PHC volume may represent a distinct neurobiological marker for PNS which could help guide future research in developing target-specific treatments.Moreover, this finding suggests that clients with PNS may represent a distinct subtype.The concept of remission may need to be reformulated to account for those presenting with PNS.v Résumé Contexte théorique : La schizophrénie est caractérisée par la présence de symptômes positifs (hallucinations, idées délirantes) et négatifs (baisse de la motivation, anhédonie).Les symptômes négatifs peuvent être classifiés comme étant soit primaires (central à la maladie) ou secondaires (p.ex.induits par des symptômes positifs, dépressifs, ou extrapyramidaux).Les symptômes négatifs primaires sont généralement associés à une issue fonctionnelle défavorable.Malgré cela, aucune avenue thérapeutique ciblant spécifiquement ceux-ci n'est disponible à ce jour.Ainsi, ce projet vise à accroître notre compréhension de ces symptômes en: 1) explorant la proportion de symptômes négatifs primaires et secondaires présents auprès des individus atteignant la rémission; 2) examinant l'observance au traitement et le niveau de conscientisation ou insight (c.à.d.: prise de conscience du trouble de santé mentale, confiance quant à la réponse à la médication et croyance en la nécessité d'un traitement) en relation avec les symptômes négatifs primaires; et 3) confirmant les marqueurs d'imagerie cérébrale de la rémission mis en lumière par des études antérieures et en explorant la présence possible de marqueurs neuronaux des symptômes négatifs primaires.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.278
Teacher spread0.238 · 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 designObservational
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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueeScholarship@McGill (McGill)Same topicModel Reduction and Neural NetworksFrench-language works237,207