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Record W2037772407 · doi:10.1016/s0924-9338(09)70770-9

Different Pathways into Panic Disorder, Agoraphobia and Specific Phobia

2009· article· en· W2037772407 on OpenAlexaff
Agnes Nocon, Tanja Brückl, Petra Zimmermann, Hildegard Pfister, Hyacinth Irving, Jürgen Rehm, Roselind Lieb, Hans‐Ulrich Wïttchen

Bibliographic record

VenueEuropean Psychiatry · 2009
Typearticle
Languageen
FieldPsychology
TopicAnxiety, Depression, Psychometrics, Treatment, Cognitive Processes
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsAgoraphobiaPanic disorderPsychopathologyPsychologyPhobiasLatent class modelSpecific phobiaPopulationVulnerability (computing)Clinical psychologyOdds ratioLogistic regressionPsychiatryMedicineAnxietyInternal medicineEnvironmental health

Abstract

fetched live from OpenAlex

Background: In light of the ongoing debate whether agoraphobia [AG] should be viewed as a severe phobic disorder similar to specific phobia [SPE] or as a complication of panic disorder [PD] we aim to study the vulnerability structure of PD, AG and SPE. Methods: 3021 14-24 year-olds from the general population were followed-up over 10 years. DSM-IV syndromes were assessed via computerized M-CIDI interview and vulnerability factors via questionnaires. Associations were assessed with odds ratios from logistic regression. Latent class analysis (LCA) regressed on vulnerability factors was used to derive classes that underlie panic and phobic syndromes and to assess their associations with vulnerability factors. Results: 1. Vulnerability patterns were largely similar between PD, AG and SPE. 2. The LCA resulted in a best fitting model with 4 classes: a healthy class, a class with moderate frequency of phobias without PD, a class characterized by PD and AG and moderate frequency of SPE (PDAG class) and one class characterized by high frequency of AG and SPE situational type and lower frequency of PD (AGSIT class). 3. All classes showed different associations with multiple vulnerability measures. Subjects in the PDAG class reported less SPE in parents (OR=0.2; 95% CI=0.0-0.6) and older onset-age of any psychopathology (OR=2.0; 95% CI=1.07-3.6) than the AGSIT class. Discussion: We found indications for separate latent classes underlying PD and phobias that were characterized by different vulnerability factors. We interprete the different classes as different vulnerability clusters and evidence of multiple pathways leading to panic and phobias.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.019
GPT teacher head0.273
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 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
Published2009
Admission routes1
Has abstractyes

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