Comparative study of three<i>flexiVent</i>system configurations using mechanical test loads
Bibliographic record
Abstract
The sole commercial system currently employing the forced oscillation technique (FOT) in small laboratory animals (flexiVent; SCIREQ Inc., Canada) was recently redesigned along with its operating software. Yet, many users still work with the legacy version or a mixed configuration. This study aimed to compare result accuracy and precision between three flexiVent system configurations and to quantify the impact of configuration changes on measured parameters. Physiologically relevant resistance or elastance were measured at 2.5 Hz on the following three system configurations using characterized mechanical test loads: (i) legacy flexiVent-flexiVent v5.3.4 (Leg-fV5), (ii) legacy flexiVent-flexiWare v7.2.1 (Leg-fW7), and (iii) flexiVent FX-flexiWare v7.2.1 (FX-fW7). Results demonstrated measurements of high precision that were consistent between system configurations. There was no statistical difference between system configurations in terms of measuring a predicted resistance. Measurements of elastance, on the other hand, were configuration-sensitive with FX-fW7 generating values that were closer to theoretical ones than the other two configurations. The largest impact on measurement outcomes was associated with the most noteworthy configuration change (i.e., software and hardware replacement). This effect was however constrained, with variations in the order of 3-5%, approximately. In conclusion, the latest version of the sole commercial pre-clinical FOT system currently available generated results that were equivalent or better than those acquired with two other system configurations. Given that configuration changes were associated with subtle parameter differences, best practice would recommend consistency within a study and reporting the full details of the system used.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".