Interventions for cancer-related pain
Bibliographic record
Abstract
BACKGROUND: Several treatments are beneficial for patients with cancer-related pain (CRP), and there are numbers of systematic reviews evaluating the effectiveness and safety of these treatments. However, the overall quality of the evidence has not been quantitatively assessed. The aim of this study is to overcome the inconclusive evidence about the interventions of CRP. METHODS: We will perform an umbrella systematic review to identify eligible randomised controlled trials (RCTs). A comprehensive literature search will be conducted in MEDLINE, EMBASE, and the Cochrane library for systematic reviews, meta-analyses and RCTs. We will describe the general information of the RCTs for participants, interventions, outcome measurements, comparisons, and results. Network meta-analysis will be developed to determine the comparative effectiveness of the treatments. RESULTS: The result of this network meta-analysis will provide direct and indirect evidence of treatments for CRP. CONCLUSION: The conclusion of our study will help clinicians and CRP patients to choose suitable treatment options. ETHICS AND DISSEMINATION: Formal ethical approval is not required, as the data are not individualized. The findings of this systematic review will be disseminated in a peer-reviewed publication and/or presented at relevant conferences. PROSPERO REGISTRATION NUMBER: CRD42019131721.
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 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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".