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Classification Systems in Orthopaedics

2002· review· en· W1949714730 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

VenueJournal of the American Academy of Orthopaedic Surgeons · 2002
Typereview
Languageen
FieldDecision Sciences
TopicReliability and Agreement in Measurement
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineReliability (semiconductor)DocumentationKappaMeasure (data warehouse)Orthopedic surgeryMedical physicsCohen's kappaReliability engineeringData miningMachine learningComputer scienceSurgery

Abstract

fetched live from OpenAlex

Classification systems help orthopaedic surgeons characterize a problem, suggest a potential prognosis, and offer guidance in determining the optimal treatment method for a particular condition. Classification systems also play a key role in the reporting of clinical and epidemiologic data, allowing uniform comparison and documentation of like conditions. A useful classification system is reliable and valid. Although the measurement of validity is often difficult and sometimes impractical, reliability-as summarized by intraobserver and interobserver reliability-is easy to measure and should serve as a minimum standard for validation. Reliability is measured by the kappa value, which distinguishes true agreement of various observations from agreement due to chance alone. Some commonly used classifications of musculoskeletal conditions have not proved to be reliable when critically evaluated.

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)0.023
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.968
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.005
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.002
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 head0.243
GPT teacher head0.416
Teacher spread0.173 · 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