<scp>Three</scp>‐dimensional reconstruction of the innervation of the female pelvis: A review of current methods
Bibliographic record
Abstract
The three-dimensional (3D) reconstruction of tissues is a valuable approach for elucidating the anatomy of nerves and plexuses, which are often microscopic in nature and therefore difficult to fully appreciate using gross dissection approaches alone. A common workflow which can be used to generate such 3D models has yet to be comprehensively described. This study aimed to review 3D reconstruction methodologies and findings related to human female pelvic innervation to determine whether there is an optimal methodology and identify the limitations of these approaches. A comprehensive literature review was conducted using keywords including 3D reconstruction, human female pelvic nerves, and innervation. Twenty relevant articles published between 2003 and 2019 were selected for review. The 3D reconstruction of female pelvic innervation generally follows two workflows involving either immunohistochemistry (IHC) (n = 16) or magnetic resonance imaging (MRI) (n = 4). There were commonalities among the general steps reported for 3D tissue reconstruction across these two imaging methodologies. Notably, there was some variability in study methodology across the studies reviewed, suggesting there is not a clear best practice for the reconstruction of these tissues. Information that generates 3D mapping of innervation has important clinical applications, such as informing and optimizing surgical approaches to avoid damage to local innervation. IHC and MRI-based approaches are both feasible for the reconstruction of pelvic innervation, though there are advantages and disadvantages to both. Information from this review can be used to help inform the development of 3D models of female pelvic innervation in the future.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".