The physician factor and anatomical site in 8846 consecutive mediastinal lymph node aspirations in a cross-sectional study
Bibliographic record
Abstract
Mediastinal lymph node fine needle aspiration (MLN-FNA) is a common procedure; however, the physician factor in pathological category, and anatomical site are not routinely assessed. Cytology reports for endobronchial ultrasound (EBUS)/endoscopic ultrasound (EUS) MLN-FNA specimens (8846) were retrieved for July 2012-Dec 2019, classified by hierarchical free text string match algorithm into 51 diagnostic categories, four mutually exclusive diagnostic groups (benign |suspicious |malignant |insufficient), and 24 anatomical sites. Pathologist and submitting physician/surgeon bias were assessed using logistic regression and funnel plots|control charts centered on the group median (diagnostic/capture) rate. Eleven pathologists and seven submitting physician/surgeon were involved in more than 250 specimens each. Overall, the MLN-FNAs were benign|suspicious|malignant|insufficient in 46%|4%|25%|24% of specimens. Percent malignant (number of samples) varied by station; 7| 4R| 4L| 2R| 10R| 11R| 11L were respectively 21%(3,101), 27%(2,453), 19%(1,289), 41%(435), 27%(497), 24%(357), 26%(229). The number of outlier (P < 0.05/P < 0.001) pathologists of 11 from the group median rate for benign|suspicious|malignant|insufficient was 0/0| 3/1| 0/0| 3/0 respectively. The outlier (P < 0.05/P < 0.001) submitting physicians/surgeons of 7 for benign|suspicious|malignant|insufficient was 3/2| 2/2| 3/2| 3/2 respectively. The physician and anatomical site are significant predictors of MLN-FNA pathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".