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Record W4311026143 · doi:10.1111/bdi.13281

Staging the bipolar disorders: Are early stages too early a stage for intervention?

2022· letter· en· W4311026143 on OpenAlexafffundabout
Anne Duffy, Charles Keown‐Stoneman

Bibliographic record

VenueBipolar Disorders · 2022
Typeletter
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsPublic Health OntarioUniversity of TorontoSt. Michael's HospitalQueen's University
FundersCanadian Institutes of Health Research
KeywordsWorryAnxietyBipolar disorderProdromePsychologyOverdiagnosisPsychiatryMoodHarmIntervention (counseling)Clinical psychologyMood disordersMedicinePsychosis

Abstract

fetched live from OpenAlex

In his Review, Parker questions the usefulness of a staging approach for bipolar disorders (BD) given the state of knowledge and warns of the risk of overdiagnosis and overtreatment of early stage presentations. Parker raises the point that childhood anxiety and sleep disorders are common, making it difficult to ascertain when these presentations represent antecedents to BD and that by identifying individuals at increased risk, unnecessary worry and possible stigma may ensue. Although concerns about going beyond the evidence are valid and core to the values of medicine to do no harm, a developmental approach to understanding the evolution of BD has been extremely informative, advancing both clinical practice and research. The Canadian Flourish longitudinal offspring study started in 19971 in direct response to questions from BD parents about the risk of illness in their children. At the time, there was insufficient data to inform accurate individual risk prediction together with an appreciation that, given the substantial genetic and phenotypic heterogeneity, the risk would vary significantly between families and among individuals within families. An unexpected finding from the Flourish offspring study was the elevated rate of childhood anxiety and sleep disorders in high risk children compared with children of well parents. The high-risk children came from mostly intact, middle-class families, with only one BD parent (i.e., the other parent had no lifetime history of mental illness). With the longer observation of more children over the peak risk period, we found evidence of an increased risk of major mood disorder of about 2.5-fold in high-risk offspring with, compared to those without, childhood anxiety and sleep disorders.2 This finding has since been independently replicated. In contradiction to concerns raised by Parker, parents found it helpful and reassuring to understand that the risk of their child(ren) developing BD was much lower than anticipated and that families could be signposted to low-intensity support when first indicated—which likely explains the low (under 10%) attrition over decades of observational research. Several groups around the world have invested in longitudinal studies of children at familial risk of mood disorders, and as a result, the approaches and precision of individualized risk prediction have advanced; taking into account heterogeneity, and being more honest about statistical uncertainty of predictions, largely related to sample sizes.3, 4 One concern raised by Parker is the possibility that variables used to assess the risk of BD may have non causal or indirect relationships with the onset of illness. While understanding causal mechanisms is important and may inform future directions in prevention and treatment, it is not necessary that variables used to assess risk be strictly causal in nature. In practice, the causal variable may be difficult to measure for various reasons; however, a proxy variable, confounded by the truly causal variable, will still contribute useful information in assessing future risk. Further, by taking a developmental approach to mapping psychopathology in children at confirmed familial risk, we have been able to develop a refined conceptual framework to advance progress in both clinical practice and research (Figure 1).5 The staging approach has provided evidence that while not all high-risk children who develop BD will manifest each and every clinical stage, a progressive forward sequence may be the most parsimonious trajectory. This developmental framework will advance studies of multi-level risk factors (epigenetic, neurobiologic, psychologic, sociologic) associated with illness onset and inform the timing and nature of prevention opportunities (i.e., parenting and family support, psychoeducation, psychosocial interventions). Interestingly, the model also considers differential trajectories and prevention needs based on familial BD subtypes: that is the developmental trajectory of episodic lithium-responsive BD compared with that of psychotic spectrum lithium-nonresponsive BD. Although we agree with Parker's concern regarding going beyond the evidence and possible misuse of the staging approach, having reliable data to inform more precise risk prediction, refined study designs to better understand BD onset, and developmentally tailored prevention targets is vital to improving outcomes—as seen in other areas of medicine such as cancer and cardiovascular care. Furthermore, understanding better one's own health risks can empower and motivate families and individuals to engage in self-management by reducing modifiable risk exposures (i.e., substance use), making healthy lifestyle choices (i.e., diet, exercise, and sleep), and strengthening resilience (i.e., healthy socioemotional coping, family functioning). In addition, identifying among children at familial risk those who might benefit most from closer monitoring and low-intensity prevention may improve outcomes. In fact, we branded our offspring research Flourish to emphasize that the majority of children at familial risk will not develop BD and highlight the importance of developmentally appropriate, common sense, low-intensity nonstigmatizing prevention with the potential for benefits that extend lifelong and possibly intergenerationally. As with most relatively novel approaches, we believe that staging models and risk calculators would benefit from further refinement and assessment in larger cohorts; however, this should not preclude their adoption in evidence-informed practice. Data sharing is not applicable to this article as no new data were created or analyzed in this study.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.562
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.271
Teacher spread0.254 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreCommentary

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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Citations1
Published2022
Admission routes3
Has abstractyes

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