Gabapentin for phantom limb pain after amputation in pediatric oncology: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Phantom limb pain (PLP) is a prevalent problem for children after amputation because of the chemotherapy treatment. Gabapentin is a potential option to manage PLP after amputation in pediatric oncology. However, no systematic review specifically investigated this topic. Thus, this study aims to appraise the efficacy and safety of gabapentin for post-amputation PLP in pediatric oncology. METHODS: Electronic databases (Cochrane Library, MEDLINE, EMBASE, Web of Science, CINAHL, PsychINFO, Scopus, WANGFANG, and Chinese Biomedical Literature Database) will be systematically searched from the beginning to the present without limitations to publication status and language. Primary outcome is pain intensity. Secondary outcomes are analgesic drug consumption, sleep quality, depression, anxiety, health-related quality of life, and adverse events. The treatment effect of all dichotomous outcome data will be estimated as risk ratio and 95% confidence intervals (CIs) and that of continuous outcome data will be calculated as mean difference or standardized mean difference and 95% CIs. Methodological quality of randomized controlled trials (RCTs) will be assessed using Cochrane risk of bias tool and that of case-controlled studies (CCSs) will be appraised using Newcastle-Ottawa Tool. Statistical analysis will be conducted using RevMan 5.3 software. DISCUSSION: This study will summarize up-to-date high-quality RCTs and CCSs to assess the efficacy and safety of gabapentin for PLP after amputation in pediatric oncology. The findings of this study will help to determine whether or not gabapentin is effective and safe for children with PLP after amputation. SYSTEMATIC REVIEW REGISTRATION: INPLASY202060090.
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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 | 0.039 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.066 | 0.007 |
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".