Use of patient-reported outcome measures after breast reconstruction in low- and middle-income countries: a scoping review
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcome measures (PROMs) are increasingly administered in high-income countries to monitor health-related quality of life of breast cancer patients undergoing breast reconstruction. Although low- and middle-income countries (LMICs) face a disproportionate burden of breast cancer, little is known about the use of PROMs in LMICs. This scoping review aims to examine the use of PROMs after post-mastectomy breast reconstruction among patients with breast cancer in LMICs. METHODS: MEDLINE, Embase, Web of Science, CINAHL, and PsycINFO were searched in August 2022 for English-language studies using PROMs after breast reconstruction among patients with breast cancer in LMICs. Study screening and data extraction were completed. Data were analyzed descriptively. RESULTS: The search produced 1024 unique studies, 33 of which met inclusion criteria. Most were observational (48.5%) or retrospective (33.3%) studies. Studies were conducted in only 10 LMICs, with 60.5% in China and Brazil and none in low-income countries. Most were conducted in urban settings (84.8%) and outpatient clinics (57.6%), with 63.6% incorporating breast-specific PROMs and 33.3% including breast reconstruction-specific PROMs. Less than half (45.5%) used PROMs explicitly validated for their populations of interest. Only 21.2% reported PROM response rates, ranging from 43.1 to 96.9%. Barriers and facilitators of PROM use were infrequently noted. CONCLUSIONS: Despite the importance of PROM collection and use in providing patient-centered care, it continues to be limited in middle-income countries and is not evident in low-income countries after breast reconstruction. Further research is necessary to determine effective methods to address the challenges of PROM use in LMICs.
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 | 0.029 | 0.156 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".