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Record W197317570

Outcomes of total joint arthroplasty in academic versus community hospitals.

2009· article· en· W197317570 on OpenAlexaffabout
Rajiv Gandhi, Peggy Tso, Aileen M. Davis, Nizar N. Mahomed

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineJoint arthroplastyJoint replacementArthroplastyQuality of life (healthcare)Physical therapyCommunity hospitalMEDLINEOsteoarthritisSurgeryAlternative medicineNursing
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Most joint-replacement surgeries are currently performed in community hospitals. We sought to determine whether the functional outcomes of joint-replacement surgery differ between academic and community hospitals. METHODS: We surveyed 471 patients for demographic data, Western Ontario McMaster University Osteoarthritis Index (WOMAC) scores and Medical Outcomes Study Short Form 36 (SF-36) scores at baseline and at 3-month and 1-year follow-up. We assessed patient satisfaction at 1 year with a single survey question. RESULTS: Community hospital patients (n = 269) were significantly older and had greater comorbidity than academic hospital patients (n = 202; p < 0.05). We found no difference in WOMAC scores, SF-36 scores or in patient satisfaction between hospitals at 1-year follow-up (p > 0.05). Adjusted analysis showed that patients undergoing surgery in an academic or community hospitals have the same functional outcomes. CONCLUSION: There is no significant difference in the functional outcomes of joint-replacement surgery between academic and community hospitals. Further work will involve evaluating cost of care differences between these types of hospitals.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.045
GPT teacher head0.282
Teacher spread0.237 · 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

Citations37
Published2009
Admission routes2
Has abstractyes

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