Intra-amniotic inflammation and child neurodevelopment: a systematic review protocol
Bibliographic record
Abstract
BACKGROUND: Intra-amniotic inflammation is associated with adverse pregnancy and neonatal outcomes. However, the impact on child neurodevelopment remains unclear. We aim to assess the effect of intra-amniotic inflammation on neurodevelopmental outcomes in children. METHODS: The databases MEDLINE, Embase, CINAHL, and Cochrane will be searched from their inception until November 2017. Randomized trials and cohort studies in which inflammatory markers were measured in amniotic fluid collected by amniocentesis and in which infant's neurodevelopment was assessed will be eligible. Two reviewers will independently select eligible studies, assess their risk of bias, and extract data. Results will be compared and a third party will be consulted in case of disagreement. Our primary outcome of interest is child neurodevelopment, assessed with either a validated tool or by revision of medical records for specific diagnosis. Secondary outcomes will include abnormal brain imaging. Relative risks will be pooled and sensitivity analyses will be performed for the indication of amniocentesis, gestational age at amniocentesis, gestational age at delivery, and fetal sex. Risk of bias will be assessed using the Cochrane Collaboration's tool for assessing the risk of bias in randomized trials or an adapted version of the ROBINS-1 for the risk of bias in non-randomized studies. DISCUSSION: This systematic review will report the current evidence regarding the association between amniotic inflammation and child neurodevelopment, and the modifiers of this association. The review will generate new hypotheses on pathological pathways and will guide future research. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2017 65065.
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 | 0.054 | 0.062 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.013 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.067 | 0.008 |
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".