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Record W1985139100 · doi:10.1002/art.10754

Association of hospital and surgeon volume of total hip replacement with functional status and satisfaction three years following surgery

2003· article· en· W1985139100 on OpenAlexaff
Jeffrey N. Katz, Charlotte B. Phillips, John A. Baron, Anne H. Fossel, Nizar N. Mahomed, Jane Barrett, Elizabeth A. Lingard, William H. Harris, Robert Poss, Robert Lew, Edward Guadagnoli, Elizabeth A. Wright

Bibliographic record

VenueArthritis & Rheumatism · 2003
Typearticle
Languageen
FieldMedicine
TopicOrthopaedic implants and arthroplasty
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersNational Institute of Arthritis and Musculoskeletal and Skin DiseasesAgency for Healthcare Research and Quality
KeywordsMedicineCohortSurgeryPopulationCohort studyOrthopedic surgeryPatient satisfactionTotal hip replacementGeneral surgeryInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate whether hospital volume and surgeon volume of total hip replacements (THRs) are associated with patient-reported functional status and satisfaction with surgery 3 years postoperatively. METHODS: We performed a population-based cohort study of a stratified random sample of Medicare beneficiaries who underwent elective primary or revision THR in Ohio, Pennsylvania, or Colorado in 1995. The primary outcomes were the self-reported Harris hip score and a validated scale measuring satisfaction with the results of surgery. Both outcomes were assessed 3 years postoperatively. Hospital volume was defined as the aggregate number of elective primary and revision THRs performed on Medicare beneficiaries in the hospital in 1995. High-volume hospitals were defined as those in which >100 such procedures are performed annually, and low-volume centers were defined as those in which </=12 procedures (primary THR cohort) or </=30 procedures (revision cohort) are performed annually. RESULTS: In unadjusted analyses, patients who underwent surgery in low-volume centers had worse functional status 3 years following primary and revision THR compared with patients whose surgery was performed in higher-volume centers. Patients whose revision THR was performed by a low-volume surgeon also had worse function. After adjustment for sociodemographic and clinical variables, however, the association between higher hospital volume and better functional status following primary THR was weak and statistically nonsignificant, and no statistically significant or clinically important associations between hospital or surgeon volume and functional status following revision THR was observed. Patients who underwent elective primary THR in low-volume centers were more likely to be dissatisfied with the results of surgery compared with patients whose surgeries were performed in high-volume centers. Similarly, patients whose surgeons performed </=12 procedures per year were more likely to be dissatisfied with the results of revision THR than were patients whose surgeons performed >12 procedures per year. CONCLUSION: Hospital volume and surgeon volume have little effect on 3-year functional outcome following THR, after adjusting for patient sociodemographic and select clinical characteristics. However, satisfaction with primary THR is greater among patients who underwent surgery in high-volume centers, and satisfaction with revisions is greater among patients whose operations were performed by higher-volume surgeons. Referring clinicians should incorporate these findings into their discussion of referral choices with patients considering THR. Conclusions regarding the effect of volume on longevity of the implants must await longer-term followup studies. Finally, further research is warranted to better understand the association between hospital and surgeon procedure volume and patient satisfaction with surgery.

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)0.005
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.197
Teacher spread0.190 · 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

Citations202
Published2003
Admission routes1
Has abstractyes

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