Bibliographic record
Abstract
Summary The forced oscillation technique (FOT) allows the characterisation of respiratorymechanics such as respiratory resistance (R rs ) and reactance (X rs ). The primaryadvantage of the FOT is that little active cooperation is needed from the subject andmeasurements are easily performed during tidal breathing with firm support of cheeksand mouth floor. Standards are available for equipment specifications and datacollection. Within-session variability should be documented, while between-sessionrepeatability is essential for the study of bronchomotor responses.Most clinically relevant information has been gained from R rs and/or X rs in thefrequency range 5–8 Hz, where R rs is mostly dependent on frictional losses in theairways and X rs is determined in great part by elastic respiratory properties. There hasbeen an increasing number of paediatric reports during the past decade, particularlyin uncooperative young children. Case–control studies have reported lung functionabnormalities in young children in chronic lung disease of prematurity, with lessconsistent data in cystic fibrosis. Findings in children with stable asthma appeardependent on inclusion criteria, with more abnormalities detected with recruitmentfrom asthma clinics than in field studies. Longitudinal studies in asthmatic childrenover weeks to months suggest the FOT and in particular X
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.105 | 0.045 |
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".