Additional file 5 of Changes in concentrations of cervicovaginal immune mediators across the menstrual cycle: a systematic review and meta-analysis of individual patient data
Bibliographic record
Abstract
Additional file 5: Figure S1. Assessment of publication bias. A Funnel plots. Symbols show the effect of the menstrual cycle (x-axis) and the standard error of that effect (y-axis, reversed). Each symbol shows an individual study. Vertical solid line shows no effect. Vertical dashed line shows the meta-estimate of effect. Diagonal dashed lines enclose the region expected to include 95% of studies based on the estimated meta-effect and the standard errors. B Results of Egger’s tests for publication bias. Figure S2. Periovulatory meta-analyses. A The log2 difference between periovulatory and follicular phases (log2-pg/mL of the follicular phase minus log2-pg/mL of the periovulatory phase). For TGF-β1, the error bars for one study and the meta-estimate extend off-scale. B The log2 difference between periovulatory and luteal phases (log2-pg/mL of the luteal phase minus log2-pg/mL of the periovulatory phase). For IL-10, the error bars for one study extend off-scale. Each row represents a different immune mediator, with the symbols showing the mean and the lines showing the 95% confidence intervals. Gray symbols indicate individual studies and black the meta-estimates as determined by inverse-variance pooling random effects models. Black filled symbols indicate p < 0.05 while white filled symbols indicate p > 0.05. Positive numbers indicate higher during the follicular or luteal phase, while negative numbers indicate higher during the periovulatory phase. Fig S3. Subgroup analysis: Does the effect of menstrual cycle differ by assay method, geographical region, or method of determining menstrual phase? A Meta-analyses, comparing all studies (black circles) to studies grouped by assay method (ELISA: blue squares; MSD: yellow triangles; Luminex: green diamonds). B Meta-analyses, comparing all studies (black circles) to studies grouped by geographical region of sample origin (Africa: blue diamonds; Europe: red squares; North America: green triangles). C Meta-analyses, comparing all studies (black circles) to studies grouped by method of menstrual cycle phasing (Days since LMP: orange squares; Progesterone: pale purple diamonds; Progesterone plus LH: dark purple triangles). Figure S4. Secondary outcomes: Method of determining menstrual cycle phase and normalization to total protein. A The standard errors of the effect sizes for the difference between menstrual cycle phases, with phases determined by days since last menstrual period (“LMP”) or serum progesterone (“Prog”). Each symbol represents an immune factor, with lines connecting the same immune factor. B The standard errors of the effect sizes for the difference between menstrual cycle phases as determined using raw concentration measurements (pg/mL) and concentrations normalized to total protein (pg/pg total protein). Each symbol represents an immune factor, with lines connecting the same immune factor. Table S1. Summary of immune mediators measured in single studies. Table S2. Summary of follicular vs. periovulatory meta-analyses. Table S3. Summary of luteal vs. periovulatory meta-analyses. Table S4. Covariates adjusted for in multivariate analysis of each study.
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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 | 0.004 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.785 | 0.026 |
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".