Bibliographic record
Abstract
Background and aims: In this case a 14 year old boy with a neuromuscular disorder[NMD] presented to PICU post-cardiopulmonary arrest with R pneumothorax and grade 4 liver laceration. Prior to this event his respiratory care was in accordance with guidelines for children with NMD. He used nocturnal non-invasive ventilation [NIV], percussions & vibration, and manually assisted cough from age 2. He added bag & mask breath stacking from age 4 and changed to mechanical insufflation-exsufflation [MI-E] at age 7. His L lung had been collapsed since age 8. Aims: Facilitate lung volume recruitment [LVR] and clear secretions using minimal pressures and no external thoracic compression. Wean the patient from invasive ventilation to nocturnal NIV. Methods: Implementation of a novel treatment technique - active-assisted deep breathing[AA-DB]. AA-DB uses positive pressure from the ventilator to assist the patient’s active efforts to achieve a breath 50% > resting tidal volume[Vt]. The treatment involves having the patient perform sets of AA-DB to a target-Vt. Results: AA-DB was an effective treatment during invasive ventilation and NIV. Ability to do AA-DB was used to guide pressure settings. Efficiency in performing AA-DB sets was used to assess readiness for extubation and weaning of NIV. AA-DB has replaced MI-E in his home program. AA-DB volumes have increased over time. He has spent fewer days in hospital this year than previous. Conclusions: AA-DB was, and continues to be, an effective treatment for this young man with NMD who was previously managed with MI-E. AA-DB warrants further investigation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.396 | 0.250 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".