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Record W2170170337 · doi:10.1302/0301-620x.94b9.28964

The classification of degenerative hip disease

2012· article· en· W2170170337 on OpenAlexaff
Henry Hamilton, John Jamieson

Bibliographic record

VenueJournal of Bone and Joint Surgery - British Volume · 2012
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsLakehead UniversityThunder Bay Regional Health Sciences Centre
Fundersnot available
KeywordsDiseaseDegenerative diseaseMedicineEtiologyClinical phenotypePhenotypePathologyGeneticsBiologyGene

Abstract

fetched live from OpenAlex

It is probable that both genetic and environmental factors play some part in the aetiology of most cases of degenerative hip disease. Geneticists have identified some single gene disorders of the hip, but have had difficulty in identifying the genetics of many of the common causes of degenerative hip disease. The heterogeneity of the phenotypes studied is part of the problem. A detailed classification of phenotypes is proposed. This study is based on careful documentation of 2003 consecutive total hip replacements performed by a single surgeon between 1972 and 2000. The concept that developmental problems may initiate degenerative hip disease is supported. The influences of gender, age and body mass index are outlined. Biomechanical explanations for some of the radiological appearances encountered are suggested. The body weight lever, which is larger than the abductor lever, causes the abductor power to be more important than body weight. The possibility that a deficiency in joint lubrication is a cause of degenerative hip disease is discussed. Identifying the phenotypes may help geneticists to identify genes responsible for degenerative hip disease, and eventually lead to a definitive classification.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.004
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.004

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 head0.256
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations6
Published2012
Admission routes1
Has abstractyes

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