Evidence Graph Analysis of Postoperative Pain Sensitization Induced by Perioperative Sleep Deprivation
Bibliographic record
Abstract
ObjectiveTo describe and evaluate the clinical studies of postoperative pain sensitization caused by sleep deprivation through the evidence map system, understand the distribution of evidence in this field, and provide reference for subsequent evidence research.MethodsA computer-based search of PubMed, EMBASE, Cochrane library, Web of Science, CNKI, Wanfang Data, VIP and Chinese Biomedical Literature Database from inception to August 2023 was conducted to obtain intervention studies, observational studies and systematic reviews/Meta-analysis of postoperative pain sensitization caused by sleep deprivation. The research characteristics and methodological quality were analyzed and evaluated. The Cochrane Handbook for Systematic Reviews, the Newcastle-Ottawa Scale (NOS) and the AMSTAR-2 scale were used to evaluate the quality of the included studies, and the evidence was comprehensively analyzed and displayed by means of bubble chart, table and text.ResultsA total of 35 observational studies (31 cohort studies and 4 case-control studies), 15 randomized controlled trials and 4 systematic reviews/Meta-analyses were included. The number of publications increased rapidly after 2018 and peaked in 2022, and clinical studies in this field mainly focused on cohort studies, with fewer randomized controlled trials and systematic reviews/Meta-analysis studies. The results of the evidence map showed that in terms of quality, 22 studies were 'high quality', 24 studies were 'medium quality', and 8 studies were 'low quality'.Thirty studies showed that sleep deprivation could induce postoperative pain sensitization. Only 2 studies suggested that sleep disorders were not significantly associated with postoperative pain sensitization, and ten studies were uncertain whether sleep deprivation could induce postoperative pain sensitization.ConclusionsOverall evidence shows that sleep deprivation can induce postoperative pain sensitization, but the evaluation dimensions are limited and the methodological quality of the included literature needs to be improved. More high-quality, large-sample and standardized clinical studies should be carried out in the future to provide better scientific basis for clinical work.
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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.036 | 0.023 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 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".