Evidence map›Paper›PMID 40491876›Full record

ReviewJournal of pain research2025

Comprehensive Review on Personalized Pain Assessment and Multimodal Interventions for Postoperative Recovery Optimization.

Jingying Xu, Xiaona Liu, Jinyan Zhao, Jingjing Zhao, Hao Li, Huanhuan Ye, Shuang Ai

Abstract readReview
In one paragraph

Review in Journal of pain research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Trial
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Review
  10. Review
  11. Review
  12. Review
  13. Article
  14. Article
  15. Review
  16. Article
  17. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jingying Xu *Department of Rehabilitation Medicine, Joint Logistics Support Force No. 964 Hospital of People's Liberation Army of China, Changchun, Jilin, 130000, People's Republic of China.
Xiaona Liu *Department of Rehabilitation Medicine, Joint Logistics Support Force No. 964 Hospital of People's Liberation Army of China, Changchun, Jilin, 130000, People's Republic of China.
Jinyan ZhaoDepartment of Rehabilitation Medicine, Joint Logistics Support Force No. 964 Hospital of People's Liberation Army of China, Changchun, Jilin, 130000, People's Republic of China.
Jingjing ZhaoDepartment of Outpatient, Joint Logistics Support Force No. 964 Hospital of People's Liberation Army of China, Changchun, Jilin, 130000, People's Republic of China.
Hao LiDepartment of Rehabilitation Medicine, Joint Logistics Support Force No. 964 Hospital of People's Liberation Army of China, Changchun, Jilin, 130000, People's Republic of China.
Huanhuan YeDepartment of Outpatient, Joint Logistics Support Force No. 964 Hospital of People's Liberation Army of China, Changchun, Jilin, 130000, People's Republic of China.
Shuang AiDepartment of Outpatient, Joint Logistics Support Force No. 964 Hospital of People's Liberation Army of China, Changchun, Jilin, 130000, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Postoperative pain management is an important determinant of patient recovery, as it directly influences rehabilitation efficiency, hospitalization duration, and the risk of postoperative complications. Despite its significance, traditional pain management strategies often fail to adequately address individual variability and the multidimensional nature of pain, thereby limiting their effectiveness. To address these limitations, we designed this comprehensive narrative review to systematically summarize relevant literature published between 2000 and 2024, from databases such as PubMed and Web of Science, with a particular focus on personalized pain assessment and multimodal interventions to optimize postoperative recovery. Personalized pain assessment, guided by the biopsychosocial model, captures the biological, psychological, and social dimensions of pain, offering a more comprehensive and individualized evaluation of patient needs. In parallel, multimodal interventions, which integrate pharmacological and non-pharmacological strategies, are designed to target multiple pain mechanisms simultaneously, thereby enhancing analgesic efficacy while minimizing adverse effects. Emerging evidence indicates that combining personalized pain assessment with multimodal interventions can significantly improve clinical outcomes, as demonstrated by reductions in postoperative pain scores by approximately 20-30%, shorter hospital stays by 1-2 days, and decreased opioid consumption by 25-40%. Notable clinical applications supporting these findings include the use of dynamic pain monitoring devices, virtual reality-based therapies, and prehabilitation programs to facilitate recovery. Building upon these findings, this review further discusses the theoretical foundations underlying personalized pain management, explores its clinical applications, and examines the practical challenges associated with its implementation. Additionally, future directions are proposed, including the development of AI-driven pain assessment tools, the promotion of interdisciplinary collaboration, and the establishment of standardized clinical protocols. Collectively, these advancements support the potential of personalized, multidimensional strategies to improve postoperative outcomes and enhance overall patient satisfaction.

Indexed as

enhanced recoverymultimodal interventionspain managementpersonalized pain assessmentpostoperative recovery

Identifiers

PMID40491876
PMCPMC12147818

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.