Standardization of the Tanner-Whitehouse bone age method in the context of automated image analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".