Accuracy of Patient Age Estimation from Frontal Chest Radiographs of Adult Patients
Bibliographic record
Abstract
Objective: The objective of this study is to evaluate the accuracy of patient age estimation from frontal chest radiographs of adult patients. Methods: 195 posterior-anterior chest radiographs without significant abnormalities were shown to 5 staff radiologists and 6 radiology residents, who were asked to provide their estimates of patient age to the nearest decade. Real patient age distribution ranged from 16 to 91 years of age. Results: On average, correct estimate of patient age decade was made in 22% of cases. Staff radiologists were overall more accurate in their estimations compared to residents. Best accuracy was achieved by the radiologist with the most years of clinical experience, however overall accuracy did not tend to correlate with number of years in practice for staff, nor years of post-graduate training for residents. Overall, patient age was most often overestimated. The least accurate estimates were made for patients younger than 20 years and older than 90. Best accuracy was seen for patients between 50 and 70 years of age. For patients between 20 and 90 years of age, overall estimates were within 11 - 15 years of their true age. There was no significant difference in accuracy of age estimation between radiographs of women and men. Conclusions: Average rate of correct age estimation to the nearest decade from normal frontal chest radiographs in our study was 22%. Staff radiologists were more accurate than radiology residents. Best estimates were made for middle-aged patients, and worst for extremes of age.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".