Bibliographic record
Abstract
Introduction: Pulmonary complications are present in nearly half of pediatric hematopoietic stem cell transplant (HSCT) recipients and can significantly impact morbidity and mortality. Reported mortality rates of HSCT patients vary widely across centers. Methods: An email survey to assess management of HSCT patient with respiratory failure was conducted from June 2012-January 2013. It was distributed to pediatric intensivists in the Pediatric Acute Lung Injury and Sepsis Investigators (PALISI) network. Results: 94 pediatric intensivists from 36 institutions in US, Canada and Australia responded. Approximately two-thirds defined delivered FiO2 of 65% or greater to be toxic. Pulse oximetry was the leading value used to adjust oxygenation. The most commonly reported starting mode of ventilation was pressure regulated volume control. The vast majority (84%) felt their group practices early initiation of protective lung strategies, and (89%) felt that their group routinely practices low tidal volume ventilation. Over 90% indicated beliefs that high peak/plateau pressures or high FIO2 are injurious to lungs, and that lung protective strategies can be beneficial. The majority of respondents (72%) felt that less than half of intubated HSCT patients get transitioned to HFOV and most (89%) indicated HFOV was used only after patients “failed” conventional mechanical ventilation (CMV). Of those that thought OI is useful as an indicator of lung disease and a tool to transition to HFOV, approximately 50% thought that value should be ≤ 20. Conclusions: There seems to be consensus that lung protective strategies and avoiding oxygen toxicities are important in HSCT patients with respiratory failure. There is a lack of consensus on what defines lung protective strategies. Better understanding of centers’ actual practices may lead to identification of important determinants in patient outcome. A move towards defining “best practice” and standardizing care may be in order to improve outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.720 | 0.614 |
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".