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Record W2031342758 · doi:10.1097/nor.0b013e318199c395

Development of the National Association of Orthopaedic Nurses Guidance Statement on Safe Patient Handling and Movement in the Orthopaedic Setting

2009· article· en· W2031342758 on OpenAlexaff
Carol A. Sedlak, Margaret O. Doheny, Audrey Nelson, Thomas Waters

Bibliographic record

VenueOrthopaedic Nursing · 2009
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsOffice of the Chief Medical Examiner
Fundersnot available
KeywordsOrthopaedic nursingOrthopedic surgeryMedicineTask forceTask (project management)Health carePatient safetyOccupational safety and healthMedical emergencyPhysical therapySurgeryEngineering

Abstract

fetched live from OpenAlex

High-risk patient-handling tasks lead to work-related musculoskeletal disorders for orthopaedic nurses and other members of the healthcare team who are involved in moving patients with orthopaedic issues. Serious consequences can arise from manually moving/lifting these patients. A task force was organized that included representatives from the National Association of Orthopaedic Nurses, the Patient Safety Center of Inquiry at the James A. Haley Veterans Administration Medical Center in Tampa, the National Institute for Occupational Safety and Health, and the American Nurses Association to identify high-risk tasks performed in the orthopaedic setting and to develop evidence-based solutions to minimize the risk of musculoskeletal disorders. High-risk tasks for moving and lifting orthopaedic patients were identified. Four orthopaedic algorithms and a clinical tool were developed by the task force to direct nurses and healthcare team members caring for orthopaedic patients through the use of scientific evidence and available safe patient-handling equipment and devices.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.644
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.011
GPT teacher head0.291
Teacher spread0.280 · 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

Citations20
Published2009
Admission routes1
Has abstractyes

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