MétaCan
Menu
Back to cohort

Original Research

2012· article· en· W83062308 on OpenAlexaff
Ember Eerena Benson, Diana E. McMillan

Bibliographic record

VenueAJN American Journal of Nursing · 2012
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsSeven Oaks General Hospital
Fundersnot available
KeywordsMedicinePerioperativePatient satisfactionAnesthesiaRandomized controlled trialHypothermiaSurgeryPhysical therapy

Abstract

fetched live from OpenAlex

BACKGROUND: Total knee arthroplasty (TKA) is a procedure with associated risks of inadvertent perioperative hypothermia and significant postoperative pain. Hypothermia may affect patients' experience of postoperative pain, although the link is not well understood. OBJECTIVE: The aim of this prospective, randomized controlled trial was to determine the efficacy of a patient-controlled active warming gown in optimizing patients' perioperative body temperature and in diminishing postoperative pain after TKA. METHODS: Thirty patients who would be undergoing TKA received either a standard hospital gown and prewarmed standard cotton blanket (n = 15) or a patient-controlled, forced-air warming gown (n = 15). RESULTS: Although pain scores were not significantly different in the two groups (P = 0.08), patients who received warming gowns had higher temperatures (P < 0.001) in the postanesthesia care unit, used less opioid (P = 0.05) after surgery, and reported more satisfaction (P = 0.004) with their thermal comfort than did patients who received standard blankets. These findings indicate that patient-controlled, forced-air warming gowns can enhance perioperative body temperature and improve patient satisfaction. Patients who use warming gowns may also need less opioid to manage their postoperative pain. CONCLUSIONS: Nurses should ensure that effective patient warming methods are employed in all patients, particularly in patients with compromised thermoregulatory systems (such as older adults), and in surgeries considered to be exceptionally painful (such as TKA).

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.235

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.132
GPT teacher head0.503
Teacher spread0.371 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations51
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueAJN American Journal of NursingSame topicThermal Regulation in MedicineFrench-language works237,207