Lipid-laden macrophage index as a marker of aspiration in children, is it reliable? A scoping review
Bibliographic record
Abstract
INTRODUCTION: A diagnostic pathway to detect aspiration is challenging and usually requires a multidisciplinary approach and a variety of tests. Lipid-laden macrophage index (LLMI) was first described in 1985 by Corwin and Irwin as a promising tool to detect aspiration. Information in the literature as well as physicians' opinions about the clinical value of the LLMI remains controversial. OBJECTIVES: To assess the clinical value and possible limitations of LLMI as a diagnostic marker for detecting aspiration in children. METHODS: Based on the available literature we thought to answer the following questions: 1. Is there a reliable cutoff value of LLMI to detect aspiration? 2. What are the limitations of LLMI? We queried 8 electronic databases: Medline, Embase, CINAHL, Cochrane, Global Health, Web of Science, Africa Wide Information, and Global Index Medicus. Studies were selected based on established study criteria. Search was limited to publications in English language including human and animal studies. Authors reviewed 2900 articles and identified 21 relevant to the studied subject. RESULTS: Research reveals different proposed cutoff values for aspirators ranging from 85 to 200 macrophages. LLMI reliability has several limitations including: inter- and intraobserver variability among pathologists scores, inability to differentiate between exogenous and endogenous lipid content, inconsistencies in the definition of the term "aspiration" in various publications. Also, studies in animal models have shown that the nature of the disease, frequency of aspiration, and the time frame when bronchoalveolar lavage (BAL) is performed, could all contribute to the overlap in LLMI in aspirators versus non-aspirators. DISCUSSION: Our research demonstrates the limitations of LLMI in distinguishing between aspirators and non-aspirators. We believe based on these findings that airway teams should audit their local data as to the value of BAL in detecting aspiration in their patient population.
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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 | 0.014 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".