Feasibility of using a patient‐reported outcome measure into clinical practice following pediatric liver transplantation: The Starzl Network experience
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) are not routinely used in clinical care by pediatric liver transplant (LT) teams. The Starzl Network for Excellence in Pediatric Transplantation (SNEPT) assessed feasibility of using a disease-specific Quality of Life (QoL) questionnaire in the ambulatory setting at 10 SNEPT sites. METHODS: A mixed methods feasibility project assessing administration processes, barriers, and user experiences with the Pediatric Liver Transplant Quality of Life (PeLTQL) tool. Iterative processes sought stakeholder feedback across four phases (Pilot, Extended Pilot, Development of a Mobile App PeLTQL version, and Pilot App use). RESULTS: A total of 149 patient-parent dyads completed the PeLTQL during LT clinic follow-up. Clinicians, parents, and patients evaluated and reported on feasibility of operationalization. Only two of 10 SNEPT sites continued PeLTQL administration after the initial two pilot phases. Reasons include limited clinical time and available personnel aggravated by the COVID-19 pandemic. In response, a mobile application version of the PeLTQL was initiated. Providing PeLTQL responses electronically was "very easy" or "easy" as reported by 96% (22/23) parents. CONCLUSIONS: Administration of a PROM into post-pediatric LT clinical care was feasible, but ongoing utilization stalled. Use of a mobile app towards facilitating completion of the PeLTQL outside of clinic hours may address the time and work-flow barriers identified.
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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 | 0.040 | 0.067 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".