Roles and applications of 3D printing in gynecology: a scoping review
Bibliographic record
Abstract
There has been an explosion of research in the area of 3D printing in the medical literature, and a multitude of uses of 3D printed models have been proposed and are being explored. Applications for 3D printing which have been identified in the literature for uses in various surgical specialities have started to be investigated in the context of gynecology. Our objective is to systematically review the literature on 3D printing in gynecology through a scoping review, to 1) outline the roles and applications of 3D printing in gynecology and 2) determine feasibility and impact of 3D printing on surgical outcomes in gynecologic surgery. Studies will be screened and assessed for eligibility by two independent reviewers, who will then extract data from studies selected for inclusion using a pre-established data extraction form. Disagreements between reviewers will be settled through discussion and consensus between the reviewers. A descriptive approach for data synthesis will be used, however quantitative data will also be assessed where available. This study will help to summarize research to date on the use of 3D printing in gynecology to help to outline the clinical relevance of it’s use in this speciality, and guide future research on the topic.
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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 | 0.020 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".