Remote monitoring of pain and symptoms using wireless technology in children and adolescents with sickle cell disease
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine (a) symptoms, (b) pain characteristics (intensity, location, quality), (c) pain medications and nonpharmacological strategies used for pain, (d) thoughts and feelings, and (e) healthcare visits. We also examined the relationship between pain and sleep. DATA SOURCES: Pain and symptoms were entered on an electronic e-Diary using a smartphone and were remotely monitored by an advanced practice registered nurse (APRN). Sixty-seven children and adolescents (10-17 years) reported mild to severe pain at home that did not require healthcare visits. Symptoms reported were (a) general symptoms such as tiredness/fatigue (34.7%), headache (20.8%), yellowing of the eyes (28.4%); (b) respiratory symptoms such as sniffling (32.9%), coughing (19.1%), changes in breathing (10.0%); and (c) musculoskeletal symptoms such as stiffness in joints (15.8%). A significant negative correlation was found between pain and sleep (r = -.387, p = .024). Factors that predict pain included previous history of sickle cell disease (SCD) related events, symptoms, and negative thoughts. CONCLUSION: Pain and multiple symptoms entered on a web-based e-Diary were remotely monitored by an APRN and prompted communications, further evaluation, and recommendations. IMPLICATIONS FOR PRACTICE: Remote monitoring using wireless technology may facilitate timely management of pain and symptoms and minimize negative consequences in SCD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".