RSV prevention and treatment in pediatric lung transplant patients: A survey of current practices among the International Pediatric Lung Transplant Collaborative
Bibliographic record
Abstract
RSV infection can be severe after pediatric lung transplantation. Strategies to prevent and treat RSV in this population are underreported. To assess the current practices, we surveyed the members of the IPLTC regarding RSV prevention and treatment strategies. Twenty-eight programs were surveyed; 18 (64.3%) responded at least partially. A median of 53 transplants (range, 8-355) occurred since inception. RSV testing occurs in asymptomatic (6/17) and symptomatic (17/17) patients. Diagnostic method is polymerase chain reaction at 13 sites and DFA at 8. Transplant candidates were received prophylaxis at 10 sites, with nine following national (5) or local (4) guidelines. All use palivizumab IM and/or IV. Recipients were received prophylaxis with palivizumab at eight centers (eight IM, one IV). Fourteen were treated for RSV (seven all patients; seven age-related). Medications include inhaled (6), oral (4), or IV (4) ribavirin, plus IVIG (9), steroids (8), and IV (2) or IM (3) palivizumab. Prevention and treatment barriers include insurance/hospital concerns, such as institutional reluctance to use inhaled ribavirin. RSV prevention and treatment strategies are diverse at pediatric lung transplant programs. Many centers offer prophylaxis (9/17) and treatments (14/17), but strategies are not uniform.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".