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Record W2951944344 · doi:10.82308/51150

The effect of duration of untreated illness on clinical severity indicators in mood disorders

2016· article· en· W2951944344 on OpenAlexaboutno aff
Nissa Lebaron

Bibliographic record

VenueOpen MIND · 2016
Typearticle
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPsychiatryBipolar disorderMoodMood disordersMedicineDepression (economics)Severity of illnessBipolar illnessAntipsychoticSchizophrenia (object-oriented programming)Age of onsetPsychologyClinical psychologyManiaInternal medicineDiseaseAnxiety

Abstract

fetched live from OpenAlex

Abstract Background. Research on duration of untreated illness in psychosis has consistently found that a longer period between psychotic symptom onset and treatment with antipsychotic medication is associated with a worse illness outcome. However in the case of mood disorders, an association between duration of untreated illness and clinical severity indicators has received little attention. It is important to investigate this association in order to determine if duration of untreated illness may be a risk factor for increased illness severity in mood disorders and to identify which aspects of illness severity are impacted. This information may be used to inform optimal design of screening models and intervention plans to reduce duration of untreated illness and its effect on illness course. Methods. Participants with diagnoses of major depression (n=72), bipolar disorder type I (n=77) or bipolar disorder type II (n=42) were recruited from the Mood Disorders Clinic of the Allan Memorial Institute in Montreal, Quebec, Canada. Mood disorder diagnosis was determined using the Structured Clinical Interview for DSM-IV Axis I Disorders (SCID). Information regarding age of onset of psychiatric symptoms, age the individual first sought help, age of first psychiatric consult, psychiatric hospitalizations, lifetime suicide attempts, psychiatric comorbidities, and periods the individual was unable to attend work or school were obtained using the SCID as well. Binary and ordinal regressions adjusted for age and sex were performed to determine the association between duration of untreated illness and clinical severity indicators. Results. A longer duration of untreated illness in mood disorders was associated with fewer psychiatric hospitalizations, a lower likelihood of suicide attempts and fewer periods the individual was unable to attend work or school, but was also associated with more psychiatric comorbidities. Individuals with bipolar disorder type II were more at risk for psychiatric comorbidities and suicide attempts than those with either major depression or bipolar disorder type I. Discussion. Our results suggest that persons with more severe mood disorder symptomatology may seek professional help earlier than do those with less severe disorders. A longer duration of untreated mood disorder may allow time for the development of more comorbidities, which could be part of an adverse coping mechanism for the initial mood disorder. The period of time in the course of a mood disorder between onset of symptoms and psychiatric treatment presents an opportune time in which health care systems could promote healthy coping strategies and self-care in order to reduce the symptoms and avoid the need for psychiatric services. Improving mental health literacy may also aid in helping individuals to be able to identify milder symptoms in themselves and address them before they affect functioning.

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)0.003
metaresearch head score (Gemma)0.018
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.376
Teacher spread0.353 · 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

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