Does level of training Influence the ability to detect hepatosplenomegaly in children with leukemia?
Bibliographic record
Abstract
BACKGROUND: Children with leukemia often have hepatosplenomegaly present. This can be diagnosed with physical examination and confirmed with ultrasound. We sought to determine if level of training influenced the ability to detect hepatosplenomegaly. METHODS: All children diagnosed with leukemia during the past 5 years were reviewed. The training level of the examiner, the documentation of hepatosplenomegaly, and the ultrasound findings were collected and analyzed. RESULTS: There were 245 examinations of the spleen and 254 of the liver. Splenomegaly was correctly diagnosed by medical students 54% of the time, by residents 81%, and by staff 79% of the time. First year residents diagnosed it correctly 68% of the time, R2s 64%, R3s 76% and R4s 86% of the time. Hepatomegaly was correctly diagnosed by medical students 44% of the time, by residents 73% and by staff 68% of the time. First year residents diagnosed it correctly 77% of the time, R2s 54%, R3s 81% and R4s 75% of the time. CONCLUSIONS: Pediatric residents had the best ability to detect hepatosplenomegaly, and were better than staff and medical students, although this was not statistically significant.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".