Lung cancer biopsies: Comparison between simple 22G, 22G upgraded and 21G needle for EBUS-TBNA
Bibliographic record
Abstract
Introduction: Novel technologies are currently used for lung cancer diagnosis.EBUS-TBNA 22G is considered one of the most important tools.However; there are still issues with the sample size.Patients and Methods: 223 patients underwent EBUS-TBNA with a 21G Olympus needle, 22GUS Mediglobe and 22GUB Mediglobe.In order to evaluate the efficiency of 22GUB novel needle design.In order to evaluate the sample size of each needle, we constructed cell blocks and measured the different number of slices from each biopsy site.Results: The 22GUB novel needle had similar and larger number of slices from each biopsy site compared to 21G needle.Discussion: Firstly as a novel methodology we used the number of slices from the constructed cell blocks in order to evaluate the sample size.Secondly, we should seek novel needle designs and not only concentrate on the volume of the sample size.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".