Complex Surgical Infants Benefit From Postdischarge Telemedicine Visits
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it