Complex Surgical Infants Benefit From Postdischarge Telemedicine Visits
Bibliographic record
Abstract
BACKGROUND: Transition from the neonatal intensive care unit (NICU) to home is challenging for caregivers of complex surgical infants. A prospective, observational cohort pilot study using telemedicine to improve transition was implemented in a quaternary level IV NICU. PURPOSE: (1) To assess, identify, and resolve patient care concerns in the immediate postdischarge period. (2) To improve caregiver knowledge and care practices. DESIGN METHODS: Caregivers of medically complex infants participated in telemedicine visits with neonatal providers within 1 week of discharge. Providers reviewed infant health, equipment use, and outpatient follow-up. Video was used to visualize the infant, home environment, and care practices. Caregivers completed a postvisit satisfaction survey. RESULTS: Ninety-three visits were performed from May 2015 to March 2017. Seventy-six percent of visits were postsurgery patients. Seventy-eight postdischarge issues were identified: medication administration (13%), respiratory (19%), feeding (33%), and surgical site (35%). Fifty percent of caregivers reported that telemedicine visits prevented an additional call or visit to a clinician; 12% prompted an earlier visit (n = 93). Caregiver satisfaction rating was high. Median estimation of total mileage saved by respondents was 1755 miles. CONCLUSIONS: Postdischarge telemedicine visits with complex surgical NICU graduates identify clinical issues, provide caregivers with support, and save travel time. Advanced practice nurses are instrumental in patient recruitment, with patient visits, and in providing postdischarge continuity of care. Barriers to implementation were identified. IMPLICATION FOR PRACTICE AND RESEARCH: A randomized controlled study is warranted to measure the value of telemedicine visits for specific patient cohorts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".