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Record W2599407993 · doi:10.1093/schbul/sbx022.119

M125. Predictors of Community Tenure in Patients With Treatment Resistant Schizophrenia Following Discharge From a Social Learning Inpatient Program

2017· article· en· W2599407993 on OpenAlexaboutno aff
Nikhil Palekar, Vassilios Latoussakis, Elizabeth M. Farley, Andrew Bloch, Marcie Katz, Sandra Parker, Jennifer Amar, Donna T. Anthony, Juan Gallego, Anthony O. Ahmed

Bibliographic record

VenueSchizophrenia Bulletin · 2017
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPositive and Negative Syndrome ScalePsychological interventionSchizophrenia (object-oriented programming)PsychologyPsychiatryClinical psychologyDistressMedicinePsychosis

Abstract

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Background: Resistance to antipsychotic treatment is a significant clinical problem in patients with schizophrenia. The Social Learning Inpatient Program at New York—Presbyterian, Westchester Division is a specialized inpatient program based on a social learning model which includes interventions such as social skills training, cognitive remediation, cognitive-behavioral therapy (CBT) for Psychosis and other token economy-based interventions. The goal of our study was to determine if treatment at our program was associated with lower re-hospitalization rates after discharge and to look for predictors of successful community tenure. Methods: 58 patients with treatment resistant schizophrenia (TRS) consented to participate in the study and were assessed at the time of discharge and then followed up with a phone call at 1 month, 3 months and 6 months to collect information regarding re-hospitalization. TRS was defined based on 2 failed adequate antipsychotic trials. Assessments at the time of discharge included the Positive and Negative Syndrome Scale (PANSS) and Montreal Cognitive Assessment (MOCA) which were administered by trained clinicians. PANSS ratings were summed into composite scales informed by the 5-factor model of the PANSS—these included Positive, Negative, Disorganization, Excitement/Agitation, and Emotional Distress subscales. We used a binary logistic regression (BLR) model to identify the fewest set of symptom variables that will predict successful community tenure amongst patients discharged from the social learning program. We used an ANOVA to compare the MOCA cognitive scores. Results: Fifty-six percent of the patients were men, 36% were Caucasian, 40% African American, 20% Hispanic and 4% were Asian. At 6 months following discharge, 23% of patients with TRS were re-hospitalized at least once and 77 % of patients maintained community tenure without re-hospitalization. The overall BLR model was significant (−2LogL = 19.54, χ2(1) = 6.86, P = .009). The selected BLR model accounted for 37.9% variance in the data (Nagelkerke R2 = 0.379) and correctly predicted the hospitalization status of 74% of patients discharged. The BLR identified the Disorganization subscale of the PANSS as the strongest predictor of re-hospitalization within 6 months. Patients who were re-hospitalized within 6 months also had lower memory subscale scores (F =30.25, P = .032) than non-re-hospitalized patients. Conclusion: Seventy-seven percent of patients discharged from the social learning program were able to maintain community tenure without re-hospitalization in the 6 months after discharge. The disorganization subscale of the PANSS and lower memory subscale scores were the strongest predictors of re-hospitalization. Specific interventions delivered in an inpatient setting targeting symptoms of disorganization and cognitive deficits might be helpful in preventing re-hospitalization in this patient population.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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".

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Citations0
Published2017
Admission routes1
Has abstractyes

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