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Record W2005032587 · doi:10.1136/ebn.11.3.79

Therapeutic Interactive Voice Response enhanced CBT gains in chronic painCommentary

2008· letter· en· W2005032587 on OpenAlexaff
Sandra LeFort

Bibliographic record

VenueEvidence-Based Nursing · 2008
Typeletter
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsChronic painInteractive voice responsePsychologyPhysical medicine and rehabilitationMedicineComputer sciencePhysical therapyTelecommunications

Abstract

fetched live from OpenAlex

M R Naylor Dr M R Naylor, University of Vermont College of Medicine, Burlington, VT, USA; magdalena.naylor@vtmednet.org In patients with chronic pain who have completed a pain coping skills programme, does Therapeutic Interactive Voice Response (TIVR) enhance maintenance of treatment gains? ### Design: randomised controlled trial. ### Allocation: unclear allocation concealment. ### Blinding: unblinded. ### Follow-up period: 8 months. ### Setting: university hospital in Vermont, USA. ### Patients: 55 patients ⩾18 years of age (mean age 46 y, 84% women) who had chronic musculoskeletal pain for ⩾6 months with severity scores ⩾4 out of 10 and had completed 11 weeks of group cognitive–behavioural therapy (CBT) for pain management. Exclusion criteria included cancer-related pain, awaiting surgery, mental illness, and cognitive or hearing impairment. ### Intervention: TIVR for 4 months plus usual care …

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.509
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.359
Teacher spread0.314 · 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 teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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
Published2008
Admission routes1
Has abstractyes

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