Understanding the fundamentals of oscillometry from a strip of lung tissue
Bibliographic record
Abstract
), for example, is the maximal volume of air that can be expelled during the first second of a forced expiratory maneuver starting from a lung inflated to total lung capacity (TLC). Although it represents a very gross measurement of lung function, it is now used to guide the diagnosis and management of many lung disorders. Metrics used in oscillometry are not as concrete. Resistance, for example, has several connotations and its proper meaning in the context of a lung probed by an external device is not always intuitive. I think that the popularization of oscillometry and its firm implementation in respiratory guidelines starts with a keen understanding of what exactly is being measured. This review is an attempt to clearly explain the basic metrics of oscillometry. In my opinion, the fundamentals of oscillometry can be understood using a simple example of an excised strip of lung tissue subjected to a sinusoidal strain. The key notion is to divide the sinusoidal reacting force from the tissue strip into two sinusoids, one in phase with the strain and one preceding the strain by exactly a quarter of a cycle. Similar notions can then be applied to a whole lung subjected to a sinusoidal flow imposed at the mouth by an external device to understand basic metrics of oscillometry, including resistance, elastance, impedance, inertance, reactance and resonant frequency.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".