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Record W2043938375 · doi:10.1097/yco.0b013e328336662e

Redefining outcome measures in schizophrenia: integrating social and clinical parameters

2010· review· en· W2043938375 on OpenAlexaff
Amresh Shrivastava, Megan Johnston, Nilesh Shah, Y Bureau

Bibliographic record

VenueCurrent Opinion in Psychiatry · 2010
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsUniversity of TorontoWestern University
Fundersnot available
KeywordsOutcome (game theory)Schizophrenia (object-oriented programming)PsychologyMainstreamClinical PracticeEveryday lifePsychiatryPsychotherapistClinical psychologyMedicinePhysical therapyPolitical science

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Schizophrenia is a complex neurobehavioral disorder for which there are many promising new treatments. There is, however, a discrepancy in outcome measure reports when they are obtained from patients, relatives, caregivers, or professionals, making it difficult to determine the level of recovery. This lack of agreement may result from limitations of the measurement tools themselves, which are not comprehensive and may be measuring different aspects of outcome. Alternatively, it could be that the conceptual understanding of outcome and recovery require development. RECENT FINDINGS: For various reasons, patients assessed as 'recovered' remain excluded from mainstream society. We are of the opinion that present outcome measures do not capture real-life situations. We propose that the concept of recovery be carefully defined and the gold standard of outcome should incorporate social and clinical parameters. We attempt to redefine recovery. Patients who have shown clinical improvement do not necessarily do well in everyday situations even though there is obvious clinical improvement. Therefore, it has been repeatedly argued that a consensus of recovery should be determined and that routine clinical practice should then adapt to the agreed criteria. SUMMARY: We argue that the outcome measures should be multidimensional and consist of at least two parameters: clinical remission and social outcome.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.902
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.000

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 head0.264
GPT teacher head0.502
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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations59
Published2010
Admission routes1
Has abstractyes

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