Comparing Physical Examination With Sonographic Versions of the Same Examination Techniques for Splenomegaly
Bibliographic record
Abstract
OBJECTIVES: To determine whether sonographic versions of physical examination techniques can accurately identify splenomegaly, Castell's method (Ann Intern Med 1967; 67:1265-1267), the sonographic Castell's method, spleen tip palpation, and the sonographic spleen tip technique were compared with reference measurements. METHODS: Two clinicians trained in bedside sonography patients recruited from an urban hematology clinic. Each patient was examined for splenomegaly using conventional percussion and palpation techniques (Castell's method and spleen tip palpation, respectively), as well as the sonographic versions of these maneuvers (sonographic Castell's method and sonographic spleen tip technique). Results were compared with a reference standard based on professional sonographer measurements. RESULTS: The sonographic Castell's method had greater sensitivity (91.7% [95% confidence interval, 61.5% to 99.8%]) than the traditional Castell's method (83.3% [95% confidence interval, 51.6% to 97.9%]) but took longer to perform [mean ± SD, 28.8 ± 18.6 versus 18.8 ± 8.1 seconds; P = .01). Palpable and positive sonographic spleen tip results were both 100% specific, but the sonographic spleen tip method was more sensitive (58.3% [95% confidence interval, 27.7% to 84.8%] versus 33.3% [95% confidence interval, 9.9% to 65.1%]). CONCLUSIONS: Sonographic versions of traditional physical examination maneuvers have greater diagnostic accuracy than the physical examination maneuvers from which they are derived but may take longer to perform. We recommend a combination of traditional physical examination and sonographic techniques when evaluating for splenomegaly at the bedside.
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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 | 0.004 | 0.031 |
| 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) | 0.001 | 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".