Vergleich von Palpation und Elastographie an Schilddrüsenknoten
Bibliographic record
Abstract
UNLABELLED: In addition to ultrasound, elastography is available for evaluation of thyroid nodules for several years. AIM: of this study was to verify a statistically significant correlation between palpation and elastography as well as between scintigraphy and elastography, respectively. PATIENTS, METHODES: 97 solitary thyroid nodules in 67 women (mean age 63.0 ± 14.8 years) and 30 men (mean age 63.4 ± 18.5 years) were colour-coded by a colour spectrum from blue (soft) via yellow to red (hard) (Sonix touch ultrasound system, Ultrasonix, Canada) with a 6-14 MHz probe. These colour codes were classified into an elastography score of ES 1 to ES 4. RESULTS: 50 nodules were not palpable, 47 were addressed as "soft" (n = 16), "indifferent" (n = 24) or "hard" (n = 7). Elastography values were higher with increasing stiffness of the palpable nodules. Medians of elastography score were for the soft nodules ES 2, for the indifferent nodules ES 2.5 and for the hard nodules ES 4. A statistically significant correlation could be confirmed by the Jonckheere-Terpstra test (p = 0.01) and Spearman's rank correlation (p = 0.03). No correlation between elastography and scintigraphic uptake could be observed (p = 0.41). CONCLUSION: In detectable nodules, palpation is correlated with elastography. Since non-palpable nodules may have differences in elasticity too, elastography can provide additional data, which may influence the further diagnostic procedures and treatment essentially. Based on these results, scintigraphy cannot be replaced by elastography.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".