Effectiveness of electrical stimulation for postoperative pain in patients with osteosarcoma
Bibliographic record
Abstract
BACKGROUND: This study aims to investigate the effectiveness and safety of electrical stimulation (ES) for postoperative pain (PPP) in patients with osteosarcoma systematically. METHODS: We will systematically search the following electronic databases from inception to the May 1, 2019: MEDILINE, Cochrane Library, EMBASE, Web of Science, Springer, and CNKI without language restrictions. All literatures of randomized controlled trials (RCTs) and case-controlled studies (CCSs) of ES for PPP in patients with osteosarcoma will be included. RevMan 5.3 software (Cochrane Community; London, UK) and STATA 15.0 software (StataCorp; College Station) will be used for statistical analysis. Cochrane risk of bias will be used for methodological quality assessment for RCTs and Newcastle-Ottawa Scale will be utilized for CCSs. RESULTS: This study will assess the clinical effectiveness and safety of ES for PPP in patients with osteosarcoma through assessing primary outcome of pain intensity and secondary outcomes of frequency of rescue analgesic use, cumulative morphine consumption, quality of recovery, as well as adverse events. CONCLUSION: This study will provide latest evidence on effectiveness and safety of ES for PPP in patients with osteosarcoma, and may also provide guidance for both clinician and further studies. DISSEMINATION AND ETHICS: This study does not require ethical approval, because it will not analyze the individual patient data. Its results are expected to be published in peer-reviewed journals. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019135790.
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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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".