Diaphragm Injury in Individuals with Airflow Obstruction
Bibliographic record
Abstract
The purpose of this study was to describe the nature of diaphragm injury, to quantify the injury and number of macrophages at the light microscopic level, and to determine their association with airflow obstruction in humans. Partial-thickness diaphragm biopsies were obtained from 21 subjects going for thoracotomy surgery (FEV(1): 74 +/- 34% predicted; range: 16 to 122% predicted). Cross sections cut from frozen diaphragm were processed with H&E or processed for immunohistochemistry using the monoclonal antibody Ber-MAC3 (DAKO Corp., Carpinteria, CA) to label macrophages. Area fractions (A(A)) or the proportions of the cross- sectional area were determined by point counting all viable fields of H&E-stained diaphragm cross sections. A(A) were 66.2 +/- 9.0% for normal muscle, 17.6 +/- 7.2% for abnormal muscle, and 16.3 +/- 4.2% for connective tissue. Percent predicted FEV(1) was inversely related to the A(A) of abnormal muscle (r = -0.53, p < 0.01) and directly related to the A(A) of normal muscle (r = 0.37, p < 0.05). The number of macrophages was not related to % predicted FEV(1) (mean +/- SD: 0.41 +/- 0.18/fiber; 52 +/- 19/mm(2)). We conclude that increasing severity of airflow obstruction is associated with an increased A(A) of abnormal diaphragm and a decreased A(A) of normal diaphragm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".