Chronic pain in breast cancer patients post mastectomy with alloplastic reconstruction: A scoping review
Bibliographic record
Abstract
INTRODUCTION: Women diagnosed with breast cancer are receiving mastectomy with implant-based reconstruction at an increasing rate. Chronic post-surgical pain can be a major concern for these patients. This review sought to address the knowledge gap on the prevalence, severity and characteristics of chronic pain in this population. METHODS: A scoping review was conducted using the Arksey and O'Malley framework. Five databases were searched using keywords. Two independent reviewers performed selection and data extraction of studies that met inclusion criteria. RESULTS: Seventeen studies were included in this review. Ten studies reported prevalence of chronic pain which ranged from 7.3%-90.9% with pooled prevalence of 26.3%. Nine studies reported severity of chronic pain using various scales and methodology; most patients' pain was not severe. Risk factors for chronic pain included axillary dissection, lack of perioperative local anaesthetic, younger age and use of a tissue expander. No studies reported on possible correlation between ethnicity and pain. Eleven different assessment tools were used to measure pain. CONCLUSION: Chronic pain following post-mastectomy implant-based breast reconstruction is prevalent, associated with specific risk factors and poorly characterised. There is a need to investigate and evaluate chronic pain in this population using validated breast cancer specific pain assessment tools.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".