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Record W3198115169

Screening Tools for Chronic Post-Surgical Pain [Internet]

2021· article· en· W3198115169 on OpenAlexaboutno aff
Charlotte Wells, Suzanne McCormack

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicAnesthesia and Pain Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineChronic painQuality of life (healthcare)OpioidPsychological interventionIncidence (geometry)Physical therapyInternal medicinePsychiatryNursing
DOInot available

Abstract

fetched live from OpenAlex

Chronic post-operative pain (or chronic post-surgical pain [CPSP]) is a potential long-term complication of surgical interventions. CPSP is generally defined as pain that develops after a surgical intervention, lasts greater than 2 months, interferes with quality of life, is a continuation of pain developed in the acute phase post-surgery, or develops after a period with no pain, localized to the surgical area, and is not caused by other factors. Prolonged post-operative pain can lead to high health care utilization and costs, poorer clinical outcomes for patients, and lower quality of life. Depending on which surgery is performed, the incidence of lasting chronic pain can be from 5% to 85%. In Ontario, chronic pain (in general) costs CA$1,742 per person in 2014 (approximately CA$10 billion per year). As CPSP is a common driver of the rates of chronic pain, CPSP contributes to a large portion of health care spending in Ontario, not including a patient’s direct or indirect out-of-pocket costs. Additionally, the development of CPSP is linked to higher rates of opioid consumption. Persistent opioid use is associated with higher mortality and morbidity, and patients taking opioids to help with CPSP still report moderate to severe pain, higher disability, and lower overall global health. As opioid use and misuse is a global epidemic, strategies to reduce the development of CPSP may lower the use of opioids for pain relief.Risk factors for the development of CPSP can include pain before and after the operation (and severity of that pain), the type of surgery being performed, posttraumatic negative affect, and pain catastrophizing (i.e., exaggeration of a negative mental health state). In 1 systematic review examining factors related to the development of CPSP, preoperative factors that showed significant correlation with pain development were younger age (excluding pediatric patients), female sex, smoking, history of depressive or anxiety symptoms, sleep difficulties, higher body mass index, preoperative pain, and use of preoperative analgesia. Other suggested factors included genetics, length of surgery, and surgical techniques (e.g., type of surgical method, amount of trauma to area, and amount of tissue handling).,It has been proposed that tools that are used to measure these factors may be applicable in both predicting patients who may develop chronic pain and in the prevention of chronic pain. Tools that can classify patients as a higher risk may help physicians tailor both treatment and preventive measures for these patients or may prompt them to provide more intensive care.Validated tools to assess risk factors associated with CPSP include psychological assessments (e.g., Hospital Anxiety and Depression Scale, Pain Anxiety Symptoms Scale, Beck Depression Inventory, Amsterdam Preoperative Anxiety and Information Scale), pain catastrophizing (e.g., pain catastrophizing scale12), pain assessments (e.g., 6-factor risk model for CPSP13), and quality of life assessments (e.g., EuroQuol 5-Dimensions Questionnaire14). However, some models used to predict chronic pain can be narrow in that they do not include multiple surgical factors (e.g., laparoscopy versus open surgery) that can contribute to CPSP; specifically, they may not provide consistent definitions of psychosocial factors leading to CPSP and may not be fully validated in certain populations. Additionally, these models may be underpowered (lacking large datasets required for accuracy) and do not identify all the factors relevant to CPSP. With the goal of using validated tools for predicting the development of CPSP, it has been suggested that, if a patient is identified to be at high risk of developing CPSP, preventive measures could be taken. Some potential measures include providing preoperative analgesia, providing selective norepinephrine and serotonin reuptake inhibitors, using laparoscopic or less invasive surgery when possible, using regional anesthesia, and providing other pain-minimizing pharmaceuticals such as IV lidocaine, ketamine, or glucocorticoids.The objective of this report is to summarize evidence regarding the clinical utility of perioperative screening or prediction tools for preventing CPSP.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.029
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.006

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.

Opus teacher head0.033
GPT teacher head0.285
Teacher spread0.252 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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