Unexplained sporadic and recurrent miscarrage in the new millennium: a critical analysis of immune mechanisms and treatments
Bibliographic record
Abstract
There have been important advances in basic science investigation of mechanisms underlying spontaneous miscarriages which lend support to empirical treatments such as intravenous immunoglobulin G and allogeneic leukocyte immunotherapy. The results from clinical trials of these and other proposed treatments have been problematic. There is only one published meta-analysis of sufficient power and appropriate stratification to qualify as Level 1 evidence, and that deals only with leukocyte immunotherapy. Here we critically review current trials and their flaws, update the meta-analysis, and comment on potential new approaches. Inadequate sample size, better definition of heterogeneity, and proper stratification to minimize the effects of heterogeneity remain as problems. Verification that the experimental or test treatment was active in producing the expected alteration in immunophysiology in the recipient is lacking in most trials; use of stored rather than fresh allogeneic leukocytes appears problematic. Hidden biases that affect trial significance emerge with critical analysis, and the focus on apparent 'high quality' of design in published reports may be misleading. We conclude that there seem to be enough patients to conduct clinical trials of sufficient size to achieve adequate power to test therapies showing promise in pilot studies, but at present, the only Level 1 evidence concerns leukocyte immunotherapy which appears to increase the chance of a live birth if given to appropriate patients.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".