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Record W2413883873 · doi:10.1097/nor.0000000000000210

A Randomized Controlled Trial of an Individualized Preoperative Education Intervention for Symptom Management After Total Knee Arthroplasty

2016· article· en· W2413883873 on OpenAlexaff
Rosemary Wilson, J. Watt-Watson, Ellen Hodnett, Joan Tranmer

Bibliographic record

VenueOrthopaedic Nursing · 2016
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsQueen's UniversityOntario College of Art and DesignSt. Lawrence CollegeThe Wilson Centre
Fundersnot available
KeywordsNauseaMedicinePhysical therapyRandomized controlled trialIntervention (counseling)ArthroplastyPsychological interventionAnesthesiaSurgeryNursing

Abstract

fetched live from OpenAlex

Pain and nausea limit recovery after total knee arthroplasty (TKA) patients. The aim of this study was to determine the effect of a preoperative educational intervention on postsurgical pain-related interference in activities, pain, and nausea. Participants (n = 143) were randomized to intervention or standard care. The standard care group received the usual teaching. The intervention group received the usual teaching, a booklet containing symptom management after TKA, an individual teaching session, and a follow-up support call. Outcome measures assessed pain, pain interference, and nausea. There were no differences between groups in patient outcomes. There were no group differences for pain at any time point. Respondents had severe postoperative pain and nausea and received inadequate doses of analgesia and antiemetics. Individualizing education content was insufficient to produce a change in symptoms for patients. Further research involving the modification of system factors affecting the provision of symptom management interventions is warranted.

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)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0090.001

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 head0.009
GPT teacher head0.292
Teacher spread0.283 · 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 designRandomized trial
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

Citations30
Published2016
Admission routes1
Has abstractyes

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