The effect of equipment dead space on lung clearance index
Bibliographic record
Abstract
Background: Functional residual capacity (FRC) measured by sulfur hexafluoride (SF 6 ) multiple breath washout (MBW) is lower than FRC measured by either nitrogen (N 2 ) MBW or body plethysmography. A lower FRC should result in a higher lung clearance index (LCI); however we found SF 6 LCI to be consistently lower than N 2 LCI (PloS One 2013). In health, the difference was most pronounced in younger children. We designed a series of experiments to investigate the potential effects of equipment dead space (Vd), which is higher with N 2 MBW, on LCI measurements. Methods: MBW was measured in triplicate in 10 healthy adults (median age 22 years) by both SF 6 MBW (Amis 2000, Innovision, Odense, Denmark) and N 2 MBW (Exhalyzer D ® , EcoMedics AG, Durnten, Switzerland) using standard Vd conditions. SF 6 MBW Vd was then increased to match that of the N 2 MBW. In 11 healthy adults we also increased N 2 MBW Vd in steps approximating 1ml/kg to mimic the spectrum of Vd to weight ratios observed in preschool children. Results: As previously described, N 2 LCI was higher than SF 6 LCI (mean difference ± SD: 0.137 ± 0.58). When SF 6 MBW Vd was increased to be equivalent to N 2 MBW Vd, SF 6 LCI increased to be higher than standard N 2 LCI (0.273 ± 0.54). When Vd was added to the N 2 MBW system, N 2 LCI increased linearly by 0.4 units for each unit increase in weight adjusted Vd. Conclusion: Differences in equipment Vd explain differences in LCI observed between SF 6 MBW and N 2 MBW. Furthermore, added Vd in the N 2 MBW system increases N 2 LCI to the same degree observed in preschool children where N 2 LCI is disproportionately higher. These findings have important implications for the interpretation of MBW data in young children. Supported by NHLBI and The Irwin Family Foundation.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".