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Record W1909653555

요추의 병변이 슬관절 전치환술의 결과에 미치는 영향

2013· article· ko· W1909653555 on OpenAlexaboutno aff
조우신, 변성은, 윤영선, 선지호

Bibliographic record

Venue대한정형외과학회지 · 2013
Typearticle
Languageko
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWOMACSSS*MedicineOsteoarthritisPhysical therapyInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

목적: 요추의 병변이 슬관절 전치환술의 결과에 미치는 영향을 알아보고자 하였다. 대상 및 방법: 2009년 8월부터 2010년 5월까지 퇴행성 슬관절염으로 단일 집도의에 의해 슬관절 전치환술을 시행받은 환자 중 87명 149예를 대상으로 수술 후 1년 및 2년에 Hospital for Special Surgery scale (HSS 점수)과 슬관절 점수, Western Ontario & McMaster Osteoarthritis Index Score (WOMAC 점수)및 Swiss Spinal Stenosis score (SSS 점수)를 측정하여 전향적 방법을 통해 조사하였다. 결과: 수술 전에 비해 슬관절 점수와 HSS 점수 및 WOMAC score는 수술 후에 월등히 향상되었고, 수술 후 1년보다 2년 추시 시 더 많이 향상되었다. 수술 후 SSS 점수와 슬관절 점수의 상관관계는 통계적으로 유의하지 않은 결과를 보인 반면, HSS 점수와 WOMAC 점수 및 슬관절 점수와 HSS 점수의 차이값은 SSS 점수와 뚜렷한 상관관계를 보였다. 결론: 요추의 병변이 슬관절 전치환술 전, 후의 평가에 영향을 주기 때문에 술 후 평가 시 요추의 병변을 반영하는 평가법과 병행하는 것이 바람직하며, 이 중 SSS 점수가 좋은 평가법이라고 생각된다.

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)0.004
metaresearch head score (Gemma)0.009
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.012
Scholarly communication0.0080.007
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.105
GPT teacher head0.350
Teacher spread0.245 · 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
Published2013
Admission routes1
Has abstractyes

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