Bibliographic record
Abstract
Commercially made exercise testing equipments are designed for testing adults with greater exercise capacity and relatively large minute ventilation compared to pediatric population. As the size of breaths becomes smaller and irregular, as in small children with pulmonary diseases, measurement delay errors may introduce errors to the final results in the exercise testing system. For this reason, a custom made Exercise System was built at the Exercise Laboratory Department at the Hospital for Sick Children. It incorporates a special algorithm that corrects measurement delay errors caused by small breaths. The algorithm calculates the lag time of the expired breath to reach the sampling port and re-aligns the gas concentration reading in time with the corresponding real-time recording of the breath. The Exercise System is currently used for clinical and research purposes. The system shows satisfactory results for adult testing; however, the system requires validation of increase in accuracy of results for testing in pediatrics, especially for ill patients with very small tidal volumes. The main objective of this study is to demonstrate that incorporating the lag time in the algorithm to process and calculate the oxygen (0₂) consumption improves the accuracy of the results in small children exercise testing. In addition, investigate the theory and operation of the Exercise System and document the system designs and testing results for publishing purposes.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.022 |
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".