Can pain be managed through the Internet? A systematic review of randomized controlled trials
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
Given the increasing penetration and health care related use of the Internet, we examined the evidence on the impact of Internet-based interventions on pain. A search of Medline, CINAHL, PsycINFO, and the Cochrane Library was conducted for literature published from 1990 to 2010 describing randomized controlled trials that assessed the effects of Internet-based interventions on patients with pain of any kind. Of 6724 citations, 17 articles were included. The studies evaluated the effects of interventions that provided cognitive and behavioral therapy, moderated peer support programs, or clinical visit preparation or follow-up support on 2503 people in pain. Six studies (35.3%) received scores associated with high quality. Most cognitive and behavioral therapy studies showed an improvement in pain (n=7, 77.8%), activity limitation (n=4, 57.1%) and costs associated with treatment (n=3, 100%), whereas effects on depression (n=2, 28.6%) and anxiety (n=2, 50%) were less consistent. There was limited (n=2 from same research group) but promising evidence that Internet-based peer support programs can lead to improvements in pain intensity, activity limitation, health distress and self-efficacy; limited (n=4 from same research group) but promising evidence that social networking programs can reduce pain in children and adolescents; and insufficient evidence on Internet-based clinical support interventions. Internet-based interventions seem promising for people in pain, but it is still unknown what types of patients benefit most. More well-designed studies with diverse patient groups, active control conditions, and a better description of withdrawals are needed to strengthen the evidence concerning the impact of Internet-based interventions on people in pain.
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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 | 0.241 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.030 | 0.010 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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