Estimation of Spleen Size With Hand‐Carried Ultrasound
Bibliographic record
Abstract
OBJECTIVES: Physical examination can identify palpable splenomegaly easily, but evaluating lesser degrees of splenomegaly is problematic. Hand-carried ultrasound allows rapid bedside assessment of patients. We conducted this study to determine whether hand-carried ultrasound can reliably assess spleen size. METHODS: Patients with varying degrees of splenomegaly were studied. Two sonographers blindly measured spleen size in each patient using either a hand-carried or conventional ultrasound device in random order. Sonographers completed a data sheet indicating the adequacy of the image, clinical measurements of enlargement, and confidence in their observations. RESULTS: Sixteen patients (10 male and 6 female; mean age ± SEM, 60 ± 4 years) were recruited. Image quality was adequate or better in all scans with conventional ultrasound and in 15 of 16 scans with hand-carried ultrasound. The greatest longitudinal measurement recorded was statistically equivalent across ultrasound techniques, with mean values of 16.4 cm (95% confidence interval, 14.8-18.0 cm) for conventional ultrasound and 15.8 cm (95% confidence interval, 14.1-17.4 cm) for hand-carried ultrasound. The correlation between measurement techniques was r = 0.89 (P < .0001). Sonographers were somewhat or very confident in the outcomes of all scans with conventional ultrasound and in 15 of 16 cases with hand-carried ultrasound. In general, it took longer for sonographers to obtain images with hand-carried ultrasound. CONCLUSIONS: We have shown that hand-carried ultrasound can be used at the point of care by trained individuals to diagnose splenomegaly. However, hand-carried ultrasound images were less likely to be judged excellent, were accompanied by less diagnostic certainty, and took longer to obtain.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".