A simplified CT scoring system in non cystic fibrosis bronchiectasis
Bibliographic record
Abstract
Introduction The currently used CT scoring system in bronchiectasis is the Bhalla score, which had been designed for scoring CT scans in cystic fibrosis. This however is too detailed for use in clinical practice. Aim The aim of this study was to compare the simplified modified Reiff score to the more detailed Bhalla score in patients with non cystic fibrosis bronchiectasis. Methods 93 consecutive HRCTs were assessed. Bhalla score was calculated. The modified Reiff score used (0-18) was based on the number of lobes (max 6 including lingula) and the severity of bronchial dilatation in comparison to the adjacent vessel (0=no bronchiectasis; 1= 1-2 times; 2= 2-3 times; 3= >three times). These scores were jointly carried out by consensus between a chest physician and radiologist blinded to the clinical status of the patient. Both scores were correlated with sputum microbiology (growth of Pseudomonas aeruginosa, MRSA or enteric gram negative organisms) and FEV1 % predicted results. Result Median FEV1 % predicted was 75% (57.7-94.3%). 35% were colonized with Pseudomonas aeruginosa, MRSA or enteric gram-negative organisms; 43% with other potentially pathogenic microorganisms and 23% with mixed normal flora. Modified Reiff Score correlated well with the Bhalla score (r2= 0.9, p<0.001) Table 1. Correlation of Modified Reiff Score with Bhalla score r2 P value PA/ MRSA/ Enteric Gram negative 0.8 <0.0001 Other potentially pathogenic microorganism 0.8 <0.0001 FEV1 <50% predicted 0.8 0.0024 FEV1 >50% predicted 0.8 <0.0001 Correlation of Bhalla and Modified Reiff score. Conclusion Simplified modified Reiff score correlate well with the more complicated Bhalla score and will be easier for clinicians to use in clinical practice.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".