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Record W2965535849 · doi:10.1080/24740527.2019.1615370

Cognitive behavioral therapy to reduce persistent postsurgical pain following internal fixation of extremity fractures (COPE): Rationale for a randomized controlled trial

2019· review· en· W2965535849 on OpenAlexaff
Matilda E. Nowakowski, Randi E. McCabe, Jason W. Busse

Bibliographic record

VenueCanadian Journal of Pain · 2019
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsImpactMcMaster UniversitySt. Joseph’s Healthcare Hamilton
FundersOrthopaedic Trauma Association
KeywordsAnxietyMedicinePopulationRandomized controlled trialPsychologyPhysical therapyPsychiatrySurgery

Abstract

fetched live from OpenAlex

Background: Approximately half of all patients who undergo surgical repair of extremity fractures report persistent postsurgical pain (PPSP) at 1-year post-surgery. Psychological factors such as anxiety, depression, catastrophization, poor coping, high somatic complaints, and pessimism about recovery are risk factors for the development of PPSP. It is possible that interventions such as cognitive behavior therapy (CBT) that target psychological factors may reduce the incidence of PPSP in this population.Aims: The current report reviews the role of psychological factors in the development of PPSP and discusses the rationale and protocol development for a multi-site randomized-controlled trial investigating the effectiveness of CBT in reducing PPSP in patients with surgically treated extremity fractures.

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)0.028
metaresearch head score (Gemma)0.036
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: none
GenreCandidate signal: Protocol · Consensus signal: none
Teacher disagreement score0.028
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.036
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.071
GPT teacher head0.377
Teacher spread0.306 · 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
GenreProtocol

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

Citations8
Published2019
Admission routes1
Has abstractyes

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