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Record W2347327937

Causes of pain in orthopedic patients and nursing countermeasure

2009· article· en· W2347327937 on OpenAlexaboutno aff
Wang Zhang-qin

Bibliographic record

VenueModern Medicine Health · 2009
Typearticle
Languageen
FieldMedicine
TopicMedical Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOrthopedic surgeryPhysical therapyOrthopedic traumaSurgery
DOInot available

Abstract

fetched live from OpenAlex

Objective:To understand the causes of pain in the orthopedic patients for providing better holistic nursing care.Methods:According to international McGill pain questionnaire method,the pain status in 268 orthopedic patients from January 2006 to 2008 was analyzed.Results:The causes of pain in the orthopedic patients mainly were trauma and operative wound(40%),inflammation(14.9%),neuropathic pain(11.7%),maligment tumor(10.2%),acute hypoxia(10%) and postamputation pain(8.4%).The pain severity mainly was grade I~V,in which,grade Ⅰ accounted for 10.46%,Ⅱ for 25%,Ⅲ for 21.01%,Ⅳ for 17.2%,and V for 34% respectively.Conclusion:Appropriate and effective nursing care has better curative efficacy on pain in the orthopedic patients.

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)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.390
Teacher spread0.348 · 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 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

Citations0
Published2009
Admission routes1
Has abstractyes

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