TOWARDS AN INTERCHANGE FORMAT FOR SPATIAL AUDIO SCENES
Bibliographic record
Abstract
Objective To explore the value of multi-phase contrast-enhanced computed tomography in the differential diagnosis of parathyroid adenoma,lymph node,and thyroid. Methods The enhanced multi-slice CT (MSCT) results of 21 parathyroid adenoma patients were analyzed,and their postoperative pathological specimens were examined. During the MSCT,the plain CT scan was recorded,along with the density of thyroid adenoma,lymph nodes,and thyroid at 35 s and 65 s (D0,D35,D65) following the injection of contrast medium. Results During the D0 phase,there was significant difference in CT values between the parathyroid adenoma and thyroid parenchyma[(45?12) HU vs.(90?15)HU,P=0.007]. According to ROC curve,75 HU,with 95.2% sensitivity and specificity,was the critical value for distinguishing the density of parathyroid adenoma and that of thyroid parenchyma. At 35 s following the injection of contrast medium,there was significant difference in the enhancement degree between parathyroid adenoma and lymph node[(182?39) HU vs.(80?20)HU,P=0.004]. According to ROC curve,111 HU,with 95.2 % sensitivity and specificity,was the critical value for distinguishing the density of parathyroid adenoma and that of lymph node 35 s following the injection of contrast medium. At 35 s to 65 s following the injection of contrast medium,the parathyroid adenoma experienced a decline in density,which was dramatically different from parathyroid adenoma,however,lymph node experienced a rise in density. Conclusion Enhanced CT measurements at different time points enable the differentiation among parathyroid adenomas,lymph nodes,and thyroid.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".