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Record W4387420991 · doi:10.1111/jcpp.13904

Subtyping at‐risk adolescents for predicting response toward insomnia prevention program

2023· article· en· W4387420991 on OpenAlexaff
Sijing Chen, Shirley Xin Li, Jihui Zhang, Siu Ping Lam, Joey Wing Yan Chan, Kate Ching Ching Chan, Albert Martin Li, Charles M. Morin, Yun Kwok Wing, Ngan Yin Chan

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2023
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsInsomniaAnxietySubgroup analysisMedicineHazard ratioPsychiatryMoodPsychologyConfidence intervalInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Previous study has shown that a brief cognitive-behavioral prevention insomnia program could reduce 71% risk of developing insomnia among at-risk adolescents. This study aimed to evaluate the differential response to insomnia prevention in subgroups of at-risk adolescents. METHODS: Adolescents with a family history of insomnia and subthreshold insomnia symptoms were randomly assigned to a 4-week insomnia prevention program or nonactive control group. Assessments were conducted at baseline, 1 week, and 6- and 12-month after the intervention. Baseline sleep, daytime, and mood profiles were used to determine different subgroups by using latent class analysis (LCA). Analyses were conducted based on the intention-to-treat approach. RESULTS: LCA identified three subgroups: (a) insomnia symptoms only, (b) insomnia symptoms with daytime sleepiness and mild anxiety, and (c) insomnia symptoms with daytime sleepiness, mild anxiety, and depression. The incidence rate of insomnia disorder over the 12-month follow-up was significantly reduced for adolescents receiving intervention in subgroup 3 compared with the controls (hazard ratio [HR] = 0.37; 95% confidence interval [CI]: 0.13-0.99; p = .049) and marginally for subgroup 2 (HR = 0.14; 95% CI: 0.02-1.08; p = .059). In addition, adolescents who received intervention in subgroups 2 and 3 had a reduced risk of excessive daytime sleepiness (subgroup 2: adjusted OR [AdjOR] = 0.45, 95% CI: 0.23-0.87; subgroup 3: AdjOR = 0.32, 95% CI: 0.13-0.76) and possible anxiety (subgroup 2: AdjOR = 0.47, 95% CI: 0.27-0.82; subgroup 3: AdjOR = 0.33, 95% CI: 0.14-0.78) compared with the controls over the 12-month follow-up. CONCLUSIONS: Adolescents at risk for insomnia can be classified into different subgroups according to their psychological profiles, which were associated with differential responses to the insomnia prevention program. These findings indicate the need for further phenotyping and subgrouping at-risk adolescents to develop personalized insomnia prevention.

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)0.005
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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.348
Teacher spread0.331 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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