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Record W4232874350 · doi:10.1142/s0218957700000045

THE RELIABILITY OF A NEW COMPUTERIZED TECHNIQUE FOR MEASURING EPIPHYSEAL PLATE ZONAL HEIGHT

2000· article· en· W4232874350 on OpenAlexaff
Aaron Glickman, Pamela L. Hudak, Joanna Yang, C. Vaughan A. Bowen

Bibliographic record

VenueJournal of Musculoskeletal Research · 2000
Typearticle
Languageen
FieldVeterinary
TopicVeterinary Orthopedics and Neurology
Canadian institutionsUniversity of TorontoToronto Western HospitalHospital for Sick Children
FundersMedical Research Council
KeywordsGeneralizability theoryEpiphyseal plateReliability (semiconductor)Observer (physics)UlnaGeologyOrthodonticsGeodesyMathematicsMedicineAnatomyStatisticsPhysics

Abstract

fetched live from OpenAlex

The objective of this study was to assess the reliability of a new computerized technique for measuring epiphyseal plate zonal height. A fully crossed two factor generalizability experiment in which the effect of observers and time on the reliability of measurements of zonal height within different regions (center and periphery) of the epiphyseal plate was examined. Photomicrographs from both the central and peripheral regions of each of the 30 canine distal ulna epiphyseal plate histological sections were considered; within each region, the proliferative and hypertrophic zones were outlined and, after calibration of the computer program, the zonal height was computed. Irrespective of whether the measures were taken from photomicrographs of the central or the peripheral region of the epiphyseal plate, results indicated higher generalizability coefficients for hypertrophic zone measurements than for those of the proliferative zone in seven of the eight intra-observer and inter-observer calculations. In addition, higher generalizability coefficients were achieved for intra-observer than for inter-observer measurement situations. It was concluded that this is a reliable technique if the sample is measured by one observer and the average of three repeated measurements is used for each specimen.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.152
GPT teacher head0.429
Teacher spread0.276 · 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 designNot applicable
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
Published2000
Admission routes1
Has abstractyes

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