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Record W2325329571 · doi:10.2106/jbjs.k.01420

Update on the State of Outcome Measurement in Total Elbow Arthroplasty Research

2013· article· en· W2325329571 on OpenAlexaff
Kelly Riedel, Dorcas Beaton

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2013
Typearticle
Languageen
FieldMedicine
TopicElbow and Forearm Trauma Treatment
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsElbowOutcome (game theory)State (computer science)ArthroplastyMedicinePhysical medicine and rehabilitationPhysical therapyComputer scienceSurgeryMathematicsMathematical economicsAlgorithm

Abstract

fetched live from OpenAlex

BACKGROUND: There is little consensus for a standard set of metrics to express outcome after total elbow arthroplasty. In order to set the stage for future work toward a core set of measurement tools, our goal was to gather a complete view of the outcomes used in total elbow arthroplasty research, the concepts of their focus, and their quality as measures of the target concept. METHODS: We reviewed the outcome measures for total elbow arthroplasty presented in the literature from 2004 to 2011 in terms of the instruments used and their concepts of focus. We reviewed the reliability, validity, and responsiveness of the prevailing measurement tools. RESULTS: Of the seventy-two articles identified, 90% (sixty-five) used elbow-specific aggregate outcome measures, which combine concepts, such as physiological variables, with symptom status and functional status. The Mayo Elbow Performance Score, or a variation of that scoring system, was used in fifty-four (75%) of the seventy-two articles. Most outcomes pertained to biological and physiological variables, with fewer outcomes focusing on symptoms, function, or overall health status. A review of the measurement properties of the elbow-specific aggregate outcome measures did not reveal one to be superior. CONCLUSIONS: Overall, total elbow arthroplasty outcomes are heterogeneous in their reporting and lack standardization. The total elbow arthroplasty literature relies on several physician-derived elbow-specific aggregate measures and focuses primarily on physiological variables. The relative merits of aggregating findings into a single scoring system versus as separate components should be explored further. Finally, consideration should be given to patient-reported outcome measures in total elbow arthroplasty research. CLINICAL RELEVANCE: This study of the current "state of practice" for outcome measurement in total elbow arthroplasty revealed gaps in the breadth of measurement and a lack of comparability in elbow scoring systems that could hinder our ability to clearly and fully understand outcome after total elbow arthroplasty. Future consensus work could address both concerns and assist in the development of a core set of outcome measures.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.194
Threshold uncertainty score0.204

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.154
GPT teacher head0.316
Teacher spread0.163 · 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.

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

Citations14
Published2013
Admission routes1
Has abstractyes

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