Ataluren, a New Therapeutic for Alpha-1 Antitrypsin–Deficient Individuals with Nonsense Mutations
Bibliographic record
Abstract
Figure 1).This injurious inflation pattern is known to cause overstretch and tidal recruitment in dependent, atelectatic lung, and thereby worsen dependent lung injury (7-9).Reverse triggering increased dependent lung stretch equivalent to that applied by VT of up to 15 ml/kg during muscle paralysis, despite a constant VT (Figure 2).Second, the magnitude of negative swing in Pes during reverse triggering is proportional to the local lung stretch in dependent lung, reflecting greater propensity to injury.Monitoring Pes or electrical activity of diaphragm could contribute in two ways.Each of these modalities increases the recognition of reverse triggering, and they quantify the magnitude of inspiratory muscle force during reverse triggering.Although reverse triggering associated with breath stacking is readily detected, in the absence of breath stacking it is likely to be unnoticed by clinicians relying on standard waveforms (e.g., airway pressure and flow).In addition, it will be underestimated when VT is controlled (4).Reverse triggering occurs in deeply sedated patients, a scenario in which clinicians consider the risk for asynchrony to be minimal (4), in contrast to easily detected vigorous effort, which is widely recognized to be harmful (2, 10).Thus, sedation may reduce harm from vigorous effort but increase the incidence of subtle and unsuspected reverse triggering.Finally, estimation of inspiratory muscle force during reverse triggering may be important to decrease dependent lung stretch and potentially lessen injury.It is uncertain whether the inflation pattern associated with reverse triggering is also observed with other ventilator modes.In conclusion, during reverse triggering, injurious inflation occurred in dependent lung despite the constant overall VT, and dependent lung stretch was proportional to the negative deflection in Pes.Early recognition of reverse triggering may be beneficial.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".