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Record W2160484478 · doi:10.3109/03014460.2011.642405

Standardization of the Tanner-Whitehouse bone age method in the context of automated image analysis

2011· article· en· W2160484478 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.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAnnals of Human Biology · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicForensic Anthropology and Bioarchaeology Studies
Canadian institutionsnot available
FundersAGE-WELL
KeywordsGold standard (test)Bone ageContext (archaeology)Standard deviationStandardizationAutomated methodMedicinePopulationOrthodonticsMathematicsArtificial intelligenceStatisticsComputer scienceInternal medicineGeography

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: The Tanner-Whitehouse (TW) method for bone age determination has been the basis for many population studies and it is used in many clinics. However, TW bone age raters can differ systematically from each other. The aim of the study was to present a new standard version of TW bone age rating implemented by the automated BoneXpert method and calibrated on the manual TW stage ratings of the First Zurich Longitudinal Study. SUBJECTS: Hand radiographs of 231 children born in 1954-1956 were recorded annually from an average age of 5-20 years. For validation, 76 X-rays of Tanner's original Gold Series from eight boys were used. RESULTS: The root mean square deviation between manual and automated TW ratings in the Zurich data was 0.67 years for boys in the TW bone age range 5-15 years and 0.63 years for girls, 5-14 years. The new standard TW rating differs systematically from two previous TW versions of the automated method, based on different raters. CONCLUSION: The new automated TW ratings show good accuracy relative to the manual ratings of the Zurich data and the Gold Series. There are significant differences between manual TW raters, an effect which is eliminated with the automated method.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.014
Scholarly communication0.0000.000
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 head0.123
GPT teacher head0.370
Teacher spread0.247 · 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