Bibliographic record
Abstract
目的探讨^18F-FDG PET/CT对纵隔淋巴结的鉴别诊断价值。方法对^18F-FDG异常摄取的纵隔淋巴结最终确诊为良性病变者9例（50枚）和恶性病变者13例（35枚）的淋巴结进行比较分析。结果良性组和恶性组淋巴结的大小、CT值、SUV值分别为1.30cm、85.54HU、5.70和2.03cm、37.03HU、7.46,两组之间有统计学差异（P〈0.01）。延迟显像前、后良性组和恶性组淋巴结SUV值分别为4.81、4.71和7.61、7.92,均无统计学意义（P〉0.05）。良性组4L（22%）、11（20%）、4R（16%）和10R（14%）为好发部位;恶性组2R（17%）、4R（17%）、4L（14%）、7（11%）和10L（11%）部位多见。良、恶性组淋巴结在PET/CT图像上有不同的表现特征。结论淋巴结大小、CT值、SUV值在良？恶性鉴别诊断中有一定参考作用;延迟显像帮助不大;掌握PET/CT影像学特征,结合病史和其他实验室检查等综合分析对纵隔18F-FDG阳性淋巴结的鉴别诊断起重要作用。
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".