The investigators convened an international panel of clinicians and researchers with experience in delirium to create a reproducible confidence metric tied to postoperative delirium (POD) assessment schedules. Nineteen experts were invited; 17 completed a structured questionnaire. The team pre-specified four assessment factors they judged most likely to influence detection: (1) number of postoperative days assessed, (2) assessment scheme (daily versus intermittent), (3) number of assessments per day, and (4) time of day when assessments were performed. Panelists ranked these factors and then rated, on a 0–10 scale, their confidence that a reported negative case would truly be negative for each modality of each factor.
Investigators converted these median ratings into weighted component scores. The weight assigned to each factor derived from the frequency of expert votes ranking that factor as most important. For studies that additionally used methods such as chart review, the panelists’ responses were used to add a pre-determined bonus (0.05, 0.1, or 0.15) to the overall score when a threshold proportion of panelists indicated increased confidence. The calculated attainable overall confidence score for any study could range from 0.215 to 0.925 as reported by the authors.
The authors interpreted the resulting confidence score as analogous to a negative predictive value (NPV) for reported no-POD cases under the study’s reported assessment scheme. Subtracting the score from 1 yields a false omission rate: the proportion of reported negatives that would be expected to be positive under an optimal assessment regimen. The methodological translation is explicit: multiply the false omission rate by the number of reported non-POD cases in a study arm to estimate the absolute number of missed POD events attributable to suboptimal assessment frequency or timing. These additional events are then added to the observed events, producing hypothetical event counts and event rates for each study arm.
Those recalculated counts feed into a sensitivity analysis comparing the original pooled meta-analysis to a hypothetical meta-analysis in which all studies effectively used optimal assessment frequency and timing. The investigators provide the algebraic equations and a stepwise guide in the manuscript and supporting files to permit replication.
To illustrate the approach, the team applied it to two existing meta-analyses: one composed of observational studies and one of randomized controlled trials. For the studies included in those worked examples, individual confidence scores—as reported by the investigators—varied widely: approximately 33–77% across the observational set and 22–70% across the RCT set. Sensitivity analyses using the simulated optimal-event counts altered pooled effect sizes, measures of heterogeneity, and the precision of pooled estimates compared with the originals, demonstrating that between-study differences in assessment cadence and duration can materially affect meta-analytic conclusions.
The authors also present a fully worked calculation for an individual study in the manuscript so that readers can follow the transformation from reported counts to hypothetical counts step by step.
The method rests on two explicit assumptions the authors documented. First, the mapping of expert confidence to an NPV proxy assumes studies come from cohorts with broadly similar underlying POD incidence; negative predictive values depend on prevalence, and the experts rated confidence assuming a typical surgical population. Second, the primary approach assumes missed POD diagnoses result only from non-optimal frequency and timing and are independent of intervention or exposure. The investigators recognised situations where that assumption may not hold and therefore offered an alternative adjustment that accounts for differential misclassification when studies report significant associations between exposure/intervention and outcome.
To test robustness, the team ran several sensitivity analyses. They re-weighted factors equally (25% each) to probe dependence on expert-assigned weights. They applied a power-transformation to the false omission rate, allowing conservative or aggressive mappings (k values described in the supplement) and re-ran analyses under those scenarios. They also described a procedure that modifies the false omission allocation between study groups when the reported effect estimate is statistically significant, in order to reduce the risk of artificially narrowing true between-group differences.
Limitations are stated and concrete. The score is expert-derived; the authors note no definitive evidence exists establishing an “optimal” frequency and timing, so the score is preliminary and context-dependent. If a study fails to report details for a factor, the investigators conservatively recommended using the lowest-confidence modality for that element, which may bias sensitivity analyses toward larger adjustments. The method presumes availability of the four factor modalities in the original reports; missing reporting constrains accuracy. All of these caveats are described in the manuscript and supporting files.
Investigators supply all equations and a step-by-step guide in the article and supplements. The practical sequence they describe is: extract each study’s modalities for the four assessment factors; compute the weighted, summed confidence score (adding the chart-review bonus if applicable); convert the score to a false omission rate (1 − score); estimate additional missed events by multiplying that rate by the count of non-POD cases in each arm; recalculate event counts and rerun the meta-analysis with those hypothetical counts; then compare pooled effect size, heterogeneity, and precision to the original. The manuscript includes worked examples and supplemental files to enable reproducibility.
The authors position the confidence score as a preliminary index of the robustness of meta-analyses that include POD outcomes when assessment frequency and timing vary across studies. They provide the equations, a worked example, and multiple sensitivity checks, while explicitly listing the method’s assumptions and reporting the planned adjustments for differential misclassification when the intervention/exposure effect is significant.