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Record W2292488082 · doi:10.1115/1.4032870

Device for Verifying the Patellar Cut During Knee Replacement Surgery

2016· article· en· W2292488082 on OpenAlexaff
Erica Rex, Emmanuel M. Illical, C. Gaudelli, Barry Wylant, Karen C. T. Ho, John G. Person, Carolyn Anglin

Bibliographic record

VenueJournal of Medical Devices · 2016
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsCadaveric spasmTotal knee replacementPatellaMedicineKnee replacementKnee surgeryComputed tomographySurgeryAnterior knee painComputer scienceOrthodonticsOrthopedic surgeryOsteoarthritis

Abstract

fetched live from OpenAlex

Cutting the kneecap (patella) in knee replacement surgery is challenging and can lead to pain and reduced function when done incorrectly. The presented device allows the surgeon to check the three-dimensional symmetry and thickness of the bone remnant before the operation is complete. Observations and measurements made on 36 resected artificial patellae and 16 resected cadaveric patellae matched well with computed tomography (CT) scans of the patellae with few exceptions; the exceptions should be addressable by changes in design and use. Average time to apply the device was 1 min.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.569
Threshold uncertainty score0.648

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.033
GPT teacher head0.307
Teacher spread0.274 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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