Unmasking The Masquerader: Endobronchial Ultrasound Features In Distinguishing Malignant And Benign Lymphnodes In A Tuberculous Endemic Country.
Bibliographic record
Abstract
Introduction: Various real-time EBUS features help differentiate malignant from benign mediastinal /hilar lymph nodes(MHLN) in lung cancer staging. However, limited data exist on their role in extra-thoracic malignancy (ETM) with MHLN/isolated suspicious MHLN. Aim: Retrospective analysis of EBUS B-mode features to distinguish malignant, tuberculous &reactive lymph nodes. Methods: Patients undergoing EBUS-TBNA(Oct 2022-24) were included(excluding lung cancer staging). Six B-mode features were assessed: small axis length, margins, central hilar sign(CHS), central necrosis sign(CNS), echotexture & conglomeration. Nonmalignant cases had a 6 month clinico radiological followup. Results: A total of 354 MHLN from 211 patients were sampled:84(24%) were malignant,104(30%) had TB &166(46%)were reactive. Comparing malignant vs reactive nodes, five B-mode features were independent malignancy predictors: small axis length>1 cm[O.R:2.23(1.30-3.83)],well-defined margins[O.R:6.44(3.61-11.47)],absent CHS[O.R:2.87(1.14-7.19)],CNS present[O.R:7.53(4.07-13.92)]& heterogeneous echotexture[O.R:3.59(1.98-6.51)],except conglomeration. Comparing malignant vs TB /benign(TB+reactive)nodes, only 3 features predicted malignancy: well-defined margins[O.R:5.06 (2.81-9.13)],heterogeneous echotexture[O.R:4.34 (2.33-8.10)]& CNS present [O.R:2.27(1.19-4.32)](p<0.001).TB vs reactive nodes showed no significant B-mode differences except size. Canada LN scores of 3/4 had O.R of 15 & 28{p<0.01}for malignancy respectively. Conclusion: EBUS B-mode features reliably differentiates malignant from benign LNs but not TB from reactive LNS, reinforcing the need for tissue samping in TB endemic countries.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".