Use of a Daily Lung Compliance Evaluation to Track Progress on ECMO: Successful Use in a Patient With Cystic Fibrosis
Bibliographic record
Abstract
Survival rates of extracorporeal membrane oxygenation (ECMO) for pediatric respiratory failure have been improving and are now about 70%. With this, traditional exclusionary criteria for ECMO may be challenged. We hypothesize that an objective evaluation of pulmonary recovery whilst on ECMO may assist in the care of high risk patients, such as those with cystic fibrosis (CF), both to strategize appropriate decannulation and avoid futility. A 19-year-old female with CF developed septic shock and MRSA-associated acute respiratory distress syndrome. After 4 days, her respiratory status deteriorated and was transitioned to veno-venous ECMO. Due to uncertainty of pulmonary recovery and survival, we instituted a “ daily lung compliance trial ” (DLCT) to objectively assess pulmonary compliance and function. This included increasing ventilatory support from “ rest settings ” to moderate non-toxic setting and assessing pulmonary pressures and compliances after 30 min. This provided objective data of lung healing. Due in part to this data, the patient was decannulated from ECMO after 11 days and successfully extubated 2 days later. ECMO can be used for CF patients with acute respiratory failure as a bridge to recovery. Using a DLCT can help guide decision making for respiratory ECMO patients with significant co-morbidities. J Med Cases. 2014;5(2):83-88 doi: http://dx.doi.org/10.14740/jmc1511w
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".