MétaCan
Menu
Back to cohort
Record W4312527720 · doi:10.5298/1081-5937-50.3.01

SPECIAL ISSUE: Full Correspondence, Biofeedback, and the Placebo Effect

2022· article· en· W4312527720 on OpenAlexaff
André LeBlanc, Patrick L. McClay

Bibliographic record

VenueBiofeedback · 2022
Typearticle
Languageen
FieldNeuroscience
TopicPain Management and Placebo Effect
Canadian institutionsConcordia UniversityJohn Abbott College
Fundersnot available
KeywordsPlaceboBiofeedbackPhenomenonPlacebo responsePsychologyCognitive psychologyPhysical medicine and rehabilitationPsychotherapistSocial psychologyMedicineEpistemologyPhilosophyAlternative medicine

Abstract

fetched live from OpenAlex

The theory of full correspondence posits that all placebo-induced effects are accompanied by corresponding subjective experiences. It was first put forward as a means of explaining the nature of the placebo effect and of reconciling the leading approaches to the phenomenon in a single overarching theory. In this paper, we examine several points of contact between full correspondence and biofeedback research and consider some of their clinical and experimental implications.

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)0.003
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.043
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.013
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.001
Science and technology studies0.0020.003
Scholarly communication0.0070.004
Open science0.0030.002
Research integrity0.0130.008
Insufficient payload (model declined to judge)0.0430.010

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 head0.012
GPT teacher head0.239
Teacher spread0.227 · 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 designNot applicable
Domainnot available
GenreEditorial

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueBiofeedbackSame topicPain Management and Placebo EffectFrench-language works237,207