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Record W3113664998 · doi:10.1016/j.jad.2020.12.172

Efficacy and acceptability of different interventions for acrophobia: A network meta-analysis of randomised controlled trials

2020· article· en· W3113664998 on OpenAlexaff
Po‐Han Chou, Ping-Tao Tseng, Yi‐Cheng Wu, Jane Pei‐Chen Chang, Yu‐Kang Tu, Brendon Stubbs, André F. Carvalho, Pao-Yen Lin, Yen-Wen Chen, Kuan‐Pin Su

Bibliographic record

VenueJournal of Affective Disorders · 2020
Typearticle
Languageen
FieldComputer Science
TopicVirtual Reality Applications and Impacts
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
FundersMinistry of Science and Technology, TaiwanTainan Municipal An-Nan HospitalChina Medical University, TaiwanChina Medical University HospitalMinistry of Education
KeywordsPsychological interventionMeta-analysisPlaceboRandomized controlled trialMedicineConfidence intervalPhysical therapyPsychiatryAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.026
metaresearch head score (Gemma)0.058
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.026
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.058
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.038
Bibliometrics0.0030.003
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.131
GPT teacher head0.387
Teacher spread0.256 · 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 designMeta-analysis
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

Citations18
Published2020
Admission routes1
Has abstractno

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