Fertility and reproductive outcomes following high‐energy pelvic fractures: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background There is a need to decipher the effect of pelvic fractures (PFs) upon female fertility and live birth rate, as data including treatment regimens in large, unselected populations remain scarce. Objectives To assess the effect of high energy PFs upon female fertility and live birth rate. Search strategy Literature search for relevant studies was performed up to March 2022 in five databases: Embase, MEDLINE, CAB Abstracts, ClinicalTrials.gov, and Google Scholar. Selection criteria Retrospective studies assessing live birth, infertility, and dyspareunia rates following PFs. Data collection and analysis Data were extracted from studies independently by two authors. The quality of the included studies was assessed using the Newcastle‐Ottawa Scale (NOS) for observational studies. Main results A total of 763 female patients of median age 27.8 years (95% CI 22–38 years) were included with median follow up of 5 years. Among PF patients, infertility hazard ratio (HR) 1.18 (95% CI 0.76–1.84, P = 0.47; I2 = 18%) and dyspareunia HR 0.60 (95% CI 0.34–1.08, P = 0.09; I2 = 66%), did not significantly differ from the age‐matched literature‐reported rates among non‐PF patients. Conclusions No significant differences of live birth, infertility, and dyspareunia rates across patients with PFs were found compared with non‐PF counterparts.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".