The impact of radiotherapy in pre-pectoral implant-based breast reconstruction: a narrative review
Bibliographic record
Abstract
Background and Objective: Pre-pectoral implant-based reconstruction (PIBR) is becoming increasingly popular. It is known that the data available so far are limited regarding the effect of post-mastectomy radiotherapy (PMRT) in PIBR. This as well true with data about effect of previous breast radiotherapy (RT). Relatively few studies were undertaken and their outcomes were released in the last few years. Some of them were designed to address impact of RT. Others just mentioned its effect on their cohort. Hence this review was undertaken to study the effect of RT (both pre- and post-mastectomy) on PIBR. Methods: A narrative review of literature was undertaken. The search strategy was limited to articles written in English language. A systematic search was performed in all electronic databases (PubMed, Web of Science, Scopus, EMBASE). All studies, over the last 10 years were included, that reported or mentioned effect of RT on patients who had PIBR. The quality of each included study was assessed with the Newcastle-Ottawa Scale (NOS). Key Content and Findings: Nineteen studies reporting on impact of RT in PIBR or mentioned its effect have met the inclusion criteria. Conclusions: PMRT appears to be well tolerated in immediate PIBR with no excess adverse effects when used with acellular dermal matrix (ADM)/mesh at least in short term. Previous RT per se is not an absolute contraindication to the procedure. More studies are needed.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".