Use of mobile health technologies for postoperative care in paediatric surgery: A systematic review
Bibliographic record
Abstract
INTRODUCTION: Mobile health (mHealth) is the use of mobile communication devices such as smartphones, wireless patient monitoring devices and tablet computers to deliver health services. Paediatric surgery patient care could potentially benefit from these technologies. This systematic review summarises the current literature on the use of mHealth for postoperative care after children's surgery. METHODS: Seven databases were searched by a senior medical librarian. Studies were included if they reported the use of mHealth systems for postoperative care for children <18 years old. Data extraction and risk of bias assessment were performed in duplicate. RESULTS: A total of 18 studies were included after screening. mHealth use was varied and included appointment or medication reminders, postoperative monitoring and postoperative instruction delivery. mHealth systems included texting systems and mobile applications, and were implemented for a wide range of surgical conditions and countries. DISCUSSION: < 0.001), decrease the rate of postoperative complications and returns to the emergency department and reliably monitor postoperative pain. mHealth systems were generally appreciated by patients. Most non-randomised and randomised studies had many methodological problems, including lack of appropriate control groups, lack of blinding and a tendency to devote more time to the care of the intervention group. mHealth systems have the potential to improve postoperative care, but the lack of high-quality research evaluating their impact calls for further studies exploring evidence-based mHealth implementation.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".