MétaCan
Menu
Back to cohort
Record W4411656842 · doi:10.51847/f5fyphp7us

10.51847/F5FyPhP7US

2000· article· en· W4411656842 on OpenAlexvenueno aff

Bibliographic record

VenueTime to knit · 2000
Typearticle
Languageen
FieldNeuroscience
TopicEEG and Brain-Computer Interfaces
Canadian institutionsnot available
Fundersnot available
KeywordsNeurofeedbackNarcoticCravingNarcotic drugsNarcotic antagonistsPsychologyMedicineAnesthesiaPsychiatryAddictionInternal medicineElectroencephalographyOpioidEmergency medicine

Abstract

fetched live from OpenAlex

The goal of this research was to study and determine effectiveness of the Neurofeedbackt therapeutic method on amount of Craving in narcotic drugs-dependent patients and research method was quasiexperimental.20 narcotic drugs-dependent male patients with40-20 year-old age range and referring to the 10 centers and addiction rehabilitation clinic of Bojnord city with the number of 80 people that 40 people were selected randomly and after the implementation of Franken ' s Craving inventory were placed in two experiment and control group by the random assignment and makeup.Patients in the experimental group received 25 sessions of Neurofeedback treatment (for 6 weeks, 4 hours per week) and patients in the control group did not receive specific treatment, and patients of two groups was measured by testing Franken's Craving at the end.The results of analysis of covariance showed that the experimental group was decreased variable of Craving in narcotic drugs than control group at the end of period.Therefore, this Neurofeedbackt therapeutic method can be effectiveness on decreasing amount of Craving in narcotic opium drugs-dependent patients.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.038
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.9620.937

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 head0.015
GPT teacher head0.217
Teacher spread0.202 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations3
Published2000
Admission routes1
Has abstractyes

Explore more

Same venueTime to knitSame topicEEG and Brain-Computer InterfacesFrench-language works237,207