Incidence, Severity and Measurement of Chronic Pain in Breast Cancer Patients Post Mastectomy with Alloplastic Reconstruction: A Scoping Review Protocol
Bibliographic record
Abstract
Introduction: Breast cancer is the most commonly diagnosed cancer in Canadian women, and mastectomies with immediate breast reconstruction using implants are being done with increasing frequency. Chronic pain is one of the major factors in patient recovery and can significantly impact patient quality of life. The aim of this scoping review is to determine what information exists regarding the incidence and severity of chronic pain in breast cancer patients who have undergone mastectomy with immediate alloplastic breast reconstruction, and how it was measured. We will examine what is known about chronic pain in this population, what tools were used to survey patients, and what facets of the pain were investigated. Methods and Analysis: The Arksey and O’Malley framework is used, following their five-step process. Specifically, 1) identify the research question, 2) identify relevant studies from databases and grey literature, 3) at least two independent verifiers identify and 4) select studies, and 5) collate, summarize, and report data. Strengths and Limitations of this Study • A scoping review examining chronic pain in breast cancer patients who have undergone mastectomy with alloplastic IBR has not been published • A methodological framework is used to ensure quality and replicability, and grey literature will be used to better understand the incidence and severity of pain in the target population • This protocol will add to existing literature on the use of scoping review to examine chronic pain among breast cancer patients undergoing mastectomy • Limitations include only reviewing papers published in the English language
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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 | 0.082 | 0.083 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.040 | 0.006 |
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.
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".