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Record W4402764429 · doi:10.4088/pcc.24m03709

Childhood Separation Anxiety Disorder and Panic Disorder

2024· article· en· W4402764429 on OpenAlexaff
Mariana Costa do Cabo, Júlio Cezar Albuquerque da Costa, Antônio Egídio Nardi, Rafael C. Freire

Bibliographic record

VenueThe Primary Care Companion For CNS Disorders · 2024
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsKingston Health Sciences CentreKingston General HospitalQueen's University
Fundersnot available
KeywordsSeparation anxiety disorderPanic disorderAnxietySeparation (statistics)PsychologyPanicClinical psychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

than those from the nonrespiratory subtype (NRS). Therefore, the primary objective centered on the comparative analysis of CSAD prevalence between RS and NRS groups, with secondary objectives focusing on the comparative assessment of RS and NRS groups and the control group. criteria, and a dimensional one, the Separation Anxiety Symptom Inventory. < .001), which shows stronger association with the RS group. As seen in logistic regression, RS patients had 3.02 more chances of having CSAD when compared the NRS group and 5.11 when compared to the control group, which shows stronger association with the RS group. This study supports the hypothesis that RS-PD is associated with CSAD, while there is a weak association between NRS-PD and CSAD. It is advisable for clinicians to screen individuals with RS-PD for symptoms of separation anxiety, as these symptoms may have a negative impact on the prognosis of PD. .

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)0.000
metaresearch head score (Gemma)0.001
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 score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.014
GPT teacher head0.312
Teacher spread0.298 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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