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Record W2159837022 · doi:10.1586/17434440.2014.916208

Associations between patient expectations of joint arthroplasty surgery and pre- and post-operative clinical status

2014· review· en· W2159837022 on OpenAlexaff
Bailey A. Dyck, Michael G. Zywiel, Anisah Mahomed, Rajiv Gandhi, Anthony V. Perruccio, Nizar N. Mahomed

Bibliographic record

VenueExpert Review of Medical Devices · 2014
Typereview
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineJoint arthroplastyArthroplastyDiseasePhysical therapySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Improvements in implant materials and designs have broadened surgical indications and improved the technical successes of joint arthroplasty surgery. Nevertheless, a small but notable proportion of patients remain dissatisfied despite technically successful surgery. Given reported associations between unfulfilled patient expectations and dissatisfaction, we performed a systematic review to investigate the current state of knowledge concerning potential associations between clinical status and patient expectations of joint arthroplasty procedures. A wide range of expectation assessment instruments was identified, some of which assessed probabilistic expectations and other value-based expectations. Consistent associations were identified between probabilistic expectations of surgery and better pre-operative disease-specific and general health status, as well as more desirable post-operative disease specific scores. In contrast, no consistent associations were identified between clinical status and value-based expectations. Fulfillment of expectations was consistently associated with superior disease-specific and general health absolute and change scores, irrespective of the expectations paradigm used.

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)0.002
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.927
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0070.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.055
GPT teacher head0.423
Teacher spread0.368 · 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.

Study designOther design
Domainnot available
GenreReview

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

Citations34
Published2014
Admission routes1
Has abstractyes

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