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A fixed-effect meta-analysis models the studies as estimating a common underlying treatment effect. In a generic inverse-variance version, independent log odds ratios $y_i$ have approximate distributions $N(\delta ,v_i)$ and are pooled with weights $1/v_i$. The supplied forest plot instead has the Mantel–Haenszel pooled odds ratio weights: for event/nonevent cells $a_i,b_i,c_i,d_i$ and total $n_i$, the normalized weights are proportional to $b_i{c}_i/n_i$. They reproduce the printed percentages and yield a pooled odds ratio about $2.338$. This is still a common-effect analysis; the two weighting methods should not be silently identified.

A random-effects meta-analysis allows different true effects: $y_i\mid {\delta }_i\sim N({\delta }_i,v_i)$ with ${\delta }_i\sim N(\mu ,{\tau }^2)$. The between-study heterogeneity variance ${\tau }^2$ adds to sampling variance, giving marginal variance $v_i+{\tau }^2$. Its pooled estimate targets the mean of the study-effect distribution rather than one identical effect. Uncertainty about heterogeneity must also be considered.

The additional estimate points in the same direction as the pooled effect: its odds ratio is about $1.90$, compared with the original pooled value about $2.34$. Its standard error is relatively large, and the approximate two-standard-error interval for its log odds ratio is $0.64\pm 1.14=(-0.50,1.78)$, or about $(0.61,5.93)$ on the odds-ratio scale. This interval includes both no effect and the existing pooled effect. Thus it supplies compatible but imprecise evidence and would tend to pull the pooled estimate somewhat toward one while adding information. A change in the main conclusion is unlikely.

For scale, a conventional inverse-variance common-effect fit to the six rounded tabulated estimates can be updated by ${\hat{\delta }}_{\mathrm{new}}=(W\hat{\delta }+wy)/(W+w)$, with new weight $w=0.57^{-2}$. This gives the new study roughly eleven percent of the combined weight, a modest effect on the estimate. An exact update of the supplied Mantel–Haenszel pooled odds ratio needs the new two-by-two table, which the reported log ratio and standard error do not uniquely specify. The qualitative compatibility conclusion does not depend on pretending that the supplied plot used inverse-variance weights.