High‐Frequency Ultrasound: A Novel Diagnostic Tool to Measure Pediatric Tonsils in 3 Dimensions
Bibliographic record
Abstract
OBJECTIVE: A wide variety of pathologies can affect the palatine tonsils. Ultrasound is a commonly used modality for assessing head and neck masses in children; however, its use in tonsillar evaluation has not been widely explored. The objective of this study was to measure 3-dimensional tonsillar size with ultrasound, in centimeters, and correlate these measurements with actual ex vivo dimensions on pathology specimens. STUDY DESIGN: We performed a prospective cohort study. SETTING: The study was set in a tertiary care children's hospital. SUBJECTS AND METHODS: Children undergoing tonsillectomy were included in the study. Transcervical high-frequency ultrasonography (HFU) was performed prior to surgery to obtain 3-dimensional measurements of the right and left palatine tonsils. Mean sizes were compared to ex vivo tonsil measurements and correlations were obtained. RESULTS: Seventy-five consecutive children underwent a transcervical HFU, with a total of 150 tonsils analyzed. The mean differences between HFU and pathology measurements were -0.08 cm and -0.24 cm for the right and left craniocaudal axes, -0.19 cm and -0.18 cm for the right and left mediolateral axes, and 0.05 cm and 0.03 cm for the right and left anteroposterior axes. Correlation coefficients between ultrasound and pathology measurements were all above 0.5. CONCLUSION: HFU can accurately measure the size of pediatric tonsils in 3 dimensions.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".