Telemedicine for Pediatric Nephrology: Perspectives on COVID-19, Future Practices, and Work Flow Changes
Bibliographic record
Abstract
Although the use of telemedicine in rural areas has increased steadily over the years, its use was rapidly implemented during the onset of the coronavirus disease 2019 (COVID-19) crisis. Due to this rapid implementation, there is a lack of standardized work flows to assess and treat for various nephrotic conditions, symptoms, treatment modalities, and transition processes in the pediatric population. To provide a foundation/suggestion for future standardized work flows, the authors of this report have developed standardized work flows using the Delphi method. These work flows were informed based on results from cross-sectional surveys directed to patients and providers. Most patients and providers were satisfied, 87% and 71%, respectively, with their telemedicine visits. Common issues that were raised with the use of telemedicine included difficulty procuring physical laboratory results and a lack of personal warmth during telemedicine visits. The work flows created based on these suggestions will both enhance safety in treating patients and allow for the best possible care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".