Decision-making Tools for Postmastectomy Breast Reconstruction: A Scoping Review
Bibliographic record
Abstract
Background: Breast reconstruction is an essential consideration for patients with breast cancer undergoing a mastectomy. Patients commonly report inadequate education as an important cause of dissatisfaction with breast reconstructive care. Information sources for breast reconstruction vary in quality, accuracy, and validity. We sought to determine what academic and nonacademic resources exist supporting decision-making for patients undergoing breast reconstruction. Methods: A search was conducted of both academic literature and nonacademic social media sources. Three academic databases and 5 social media platforms were searched using keywords. Three independent reviewers performed the selection and data extraction of sources that met the inclusion criteria. Results: A total of 1172 academic articles and 1419 nonacademic records were screened, with 14 and 9 included for final review, respectively. Of the 5 nonacademic mediums searched, none were included from TikTok and Instagram. One decision-making tool (DMT) was included from Twitter, 4 from YouTube, and 4 from Google. Overall, the quality of available DMTs was very good. The one included academic DMT had a mean DISCERN score of 5, whereas the 4 DMTs from Google and Twitter had a median DISCERN score of 4. YouTube videos were ranked using the modified DISCERN tool with a median score of 5. Conclusions: Accessibility was found to be a significant barrier for patients in academic and nonacademic platforms with significant knowledge required to effectively search these platforms for resources. Efforts must be made to improve accessibility and awareness of these DMTs, as such tools are essential in shared decision-making.
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 | 0.033 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.024 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".