ArticleBMJ open2026
Protocol for Personalised Prediction of Persistent Postsurgical Pain.
Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04864275 (Personalized Prediction of Persistent Postsurgical Pain), which is not on this map. Not yet cited in PubMed.
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.
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.
Personalized Prediction of Persistent Postsurgical Pain
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPersistent postsurgical pain (PPSP) affects up to 15% of patients after major surgery, impairing physical function, quality of life and increasing risk for long-term opioid use. Current PPSP prediction models rely on static or retrospective data and fail to incorporate dynamic perioperative factors. The Personalised Prediction of Persistent Postsurgical Pain (P5) study aims to develop individualised, multimodal prediction models by integrating preoperative behavioural, psychophysical and neurocognitive assessments and high-frequency symptom monitoring. METHODS AND ANALYSIS: P5 is a prospective, single-centre cohort study enrolling 2500 adults aged 18-75 undergoing major surgery at a tertiary academic hospital. Participants complete baseline surveys, cognitive testing and quantitative sensory testing preoperatively. Ecological momentary assessments (EMAs) are collected via smartphone three times per day through 30 days postoperatively, capturing pain, mood, catastrophising and medication use. Participants are assessed on postoperative day 1 and complete online surveys at 3 and 6 months, evaluating pain persistence, interference, neuropathic symptoms and related outcomes. Clinical and perioperative data are extracted from the electronic health record. The primary outcome is PPSP at 3 months. Predictive models will be developed using supervised machine learning and dynamic structural equation modelling to extract latent features from EMA data. Model performance will be assessed using area under the receiver operating characteristic curve, area under the precision-recall curve and SHapley Additive exPlanations for interpretability. ETHICS AND DISSEMINATION: This study has received ethics approval from the Washington University School of Medicine Institutional Review Board #202101123. Informed consent is required. Results will be submitted for publication in peer-reviewed journals and presented at research conferences. TRIAL REGISTRATION NUMBER: NCT04864275.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.