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Three orthopaedic operations, over 1,000 randomized controlled trials, in over 100,000 patients

2022· article· en· W4206630232 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBone and Joint Research · 2022
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsAnterior cruciate ligamentOrthopedic surgeryArthroplastyTotal knee arthroplastyAnterior Cruciate Ligament InjuriesLower limbLigament

Abstract

fetched live from OpenAlex

In orthopaedic surgery, the three most commonly performed operations are total knee arthroplasty (TKA), total hip arthroplasty (THA), and anterior cruciate ligament reconstruction (ACLR). The demand for TKA is ever-increasing, with an estimated 700,000 TKAs performed each year in the USA alone, and a projected increase in demand to over 3.48 million procedures by 2030.1,2 Similarly, THA is one of the most successful and cost-effective interventions in orthopaedic surgery, considered by many as the operation of the century.3 The demand for THA is also rising worldwide.4-6 The anterior cruciate ligament (ACL) is the most commonly injured ligament in the knee with an estimated 400,000 ACLRs performed each year worldwide.7-9 Despite the success of and high demand for these procedures, debate continues on many surgical and technical aspects of these operations.

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.

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)0.021
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.202
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.062
GPT teacher head0.357
Teacher spread0.296 · 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