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The Natural History of Knee Osteoarthritis: India-based Knee Osteoarthritis Evaluation (iKare): A Study Protocol

2013· article· en· W2062895476 on OpenAlexaff
Chuan Silvia Li, Parag Sancheti, Beate Hanson, Mandeep Singh Dhillon, Nishith Shah, Vijay Shetty, Gurava Reddy, Jairam Jagiasi, Anil Kumar Madikere Raghunatha Reddy, Utsav Ganguly, Neelam Jhangiani, Mohit Bhandari

Bibliographic record

VenueJournal of Long-Term Effects of Medical Implants · 2013
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsHamilton General HospitalMcMaster University
Fundersnot available
KeywordsMedicineKnee painOsteoarthritisPhysical therapyOrthopedic surgeryObservational studyNatural historyPatellaInclusion and exclusion criteriaSurgeryAlternative medicineInternal medicine

Abstract

fetched live from OpenAlex

METHODOLOGY: Multi-center, cross-sectional, observational study. STUDY CENTER(S): Multiple centers in India. NUMBER OF PARTICIPANTS: 1,000. PRIMARY RESEARCH OBJECTIVE: To characterize patients and treatment utilized for orthopedic patients presenting to both private and public hospital centers in India with knee pain and symptoms suggestive of knee arthritis. INCLUSION CRITERIA: All patients 18 years of age or older who present to a recruiting hospital for treatment of knee pain will be eligible for participation. The subjects must be able to understand and complete the questionnaire. EXCLUSION CRITERIA: Patients with total knee replacement, open wound or evidence of recent surgery, or with a current or a history of tumor and/or fracture in the tibial plateau, femoral condyle or patella, in the affected knee are not eligible. STUDY OUTCOMES: This study aims to characterize the following: general demographics of patients presenting with knee pain, severity of knee symptoms at time of presentation, severity of knee pathology at time of presentation, factors associated with the decision to seek medical care, previous treatments and health care contacts, planned treatment, and gaps in treatment perceived by the patient and treating surgeons.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 integrity0.0000.001
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 head0.014
GPT teacher head0.307
Teacher spread0.294 · 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 designOther design
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

Citations0
Published2013
Admission routes1
Has abstractyes

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