Evidence map›Paper›PMID 42317570›Full record

ArticleFrontiers in pain research (Lausanne, Switzerland)2026

Postoperative pain and surgical recovery: are we treating pain or building resilience?

Cecília Daniele de Azevedo Nobre, Bruno Augusto Parada, Bruno Vítor Martins Santiago, Hazem Adel Ashmawi

Abstract read
In one paragraph

Article in Frontiers in pain research (Lausanne, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Cecília Daniele de Azevedo NobreDepartment of Anesthesiology, Rio de Janeiro State University, Rio de Janeiro, Brazil.
Bruno Augusto ParadaDepartment of Anesthesiology, Rio de Janeiro State University, Rio de Janeiro, Brazil.
Bruno Vítor Martins SantiagoDepartment of Anesthesiology, Rio de Janeiro State University, Rio de Janeiro, Brazil.
Hazem Adel AshmawiDepartment of Anesthesiology, Universidade de São Paulo, São Paulo, Brazil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Postoperative recovery remains highly variable despite advances in surgical techniques and perioperative care. Contemporary strategies, including Enhanced Recovery After Surgery (ERAS) protocols and multimodal analgesia, have improved outcomes but do not fully explain interindividual differences in recovery trajectories. Current perioperative models emphasize stress attenuation and symptom control but insufficiently address the mechanisms underlying the transition from acute postoperative pain to chronic pain and long-term disability. Surgical resilience is conceptualized as a dynamic, multidimensional adaptive process reflecting the patient's capacity to maintain or regain functional, psychological, and physiological equilibrium in response to surgical stress over time. Rather than a fixed trait, it emerges from the interaction between biological reserve, psychological adaptability, and behavioral engagement, modulated by contextual factors such as social support and access to care. Within this context, postoperative pain is not merely a symptom but a modifiable factor influencing recovery through physiological and behavioral mechanisms. In this perspective article, we emphasize the implementation of a multidimensional model integrating biological, psychological, behavioral, and clinical domains, in which pain management acts as a central modulator of recovery trajectories. This perspective highlights a critical gap in perioperative research and supports the transition toward personalized, resilience-oriented strategies.

Indexed as

pain modulationpatient-centered outcomesperioperative carepostoperative recoveryprehabilitationsurgical resilience

Identifiers

PMID42317570
PMCPMC13272462

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Registered trials

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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.