Lung Ultrasound for Pleural Line Abnormalities, Confluent <scp>B‐Lines,</scp> and Consolidation
Bibliographic record
Abstract
OBJECTIVES: Discrete B-lines have clear definitions, but confluent B-lines, consolidations, and pleural line abnormalities are less well defined. We proposed definitions for these and determined their reproducibility using COVID-19 patient images obtained with phased array probes. METHODS: Two raters collaborated to refine definitions, analyzing disagreements on 107 derivation scans from 10 patients. Refined definitions were used by those raters and an independent rater on 1260 validation scans from 105 patients. Reliability was evaluated using intraclass correlation coefficients (ICC) or Cohen's kappa. RESULTS: The agreement was excellent between collaborating raters for B-line abnormalities, ICC = 0.97 (95% confidence interval [CI] 0.97-0.98) and pleural line to consolidation abnormalities, ICC = 0.90 (95% CI 0.87-0.92). The independent rater's agreement for B-line abnormalities was excellent, ICC = 0.97 (95% CI 0.96-0.97) and for pleural line to consolidation was good, ICC = 0.88 (95% CI 0.84-0.91). Agreement just on pleural line abnormalities was weak (collaborators, κ = 0.54, 95% CI 0.48-0.60; independent, κ = 0.54, 95% CI 0.49-0.59). CONCLUSION: With proposed definitions or via collaboration, overall agreement on confluent B-lines and pleural line to consolidation abnormalities was robust. Pleural line abnormality agreement itself was persistently weak and caution should be used interpreting pleural line abnormalities with only a phased array probe.
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 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 | 0.002 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".