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Record W2984623712 · doi:10.1016/j.ijso.2019.10.009

Characteristics of coexisting patellofemoral joint osteoarthritis and tibiofemoral joint osteoarthritis in an Indonesian population: A cross-sectional study at a tertiary teaching hospital

2019· article· en· W2984623712 on OpenAlexaboutno aff
Ludwig AP. Pontoh, Anggaditya Putra, Ismail Hadisoebroto Dilogo, Toto Suryo Efar

Bibliographic record

VenueInternational Journal of Surgery Open · 2019
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineOsteoarthritisJoint diseasePhysical therapyPatellofemoral jointKnee JointCross-sectional studyTertiary carePatellaOrthodonticsInternal medicineSurgeryPathology

Abstract

fetched live from OpenAlex

ABSTRACT Introduction: Historically, the diagnosis and treatment of osteoarthritis has been focused on the tibiofemoral joint solely. For the last two decades, the role of patellofemoral joint and its involvement on the degenerative joint disease has been investigated. To date, no data existed regarding patellofemoral osteoarthritis in our country, Indonesia. Methods: We performed a cross sectional study comprising of patients diagnosed with knee osteoarthritis in Fatmawati General Hospital, a tertiary teaching hospital in Jakarta, Indonesia. The subjects underwent knee radiograph from anteroposterior, lateral and skyline views. Results: A total of 66 subjects were included, 80% of the subjects were diagnosed with combined patellofemoral and tibiofibular joint osteoarthritis Kellgren-Lawrence grade III-IV. The Western Ontario and McMaster Universities Osteoarthritis Index score was measured 69.3 points, as this might be correlated with the advancement of the disease. Conclusions: Combined patellofemoral and tibiofemoral osteoarthritis constitutes a large portion of patients with knee osteoarthitis. Highlights:

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)0.001
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 score0.022
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.286
Teacher spread0.245 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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