Christopher Kabrhel

Diagnostic information measures how much uncertainty a test is expected to resolve before its result is known, using sensitivity, specificity, and a stated starting disease probability; worked examples show how starting probability changes expected learning, why tests with similar conventional accuracy can provide different information, and how that expectation differs from the evidence supplied by an individual result; across 273 pooled diagnostic profiles from 210 reviews, we place this established measure on an empirical reference scale for interpreting and reporting diagnostic information across tests and thresholds, with the median profile resolving 29.2% of starting uncertainty at a standardised 20% starting probability.

This ConductScience registration records a diagnostic-information evidence map and secondary methodological synthesis. The study was registered internally with ConductScience on 16 August 2026. The public protocol record and DOI were established on 13 September 2026. The completed primary reference scale includes 273 pooled estimates from 210 reviews and 4,104 study-result appearances. Protocol version 1.2.1 documents the completed methods, dated amendments, authors, declarations, software, seven source inventories and supporting-code access. The internal registration date and subsequent public posting and DOI creation are distinct events.

Objectives To quantify complementary meanings of binary clinical calculator results: information beyond the observed outcome frequency, post-result risk, information direction, and precision. Design Catalogue-based cross-sectional analysis of published aggregate performance data, with a reproducible row-level audit. Setting The 847-calculator MDCalc catalogue and a fixed collection of publicly available reports assembled without a systematic literature search. Participants Evaluations comparing a binary calculator result with a binary clinical outcome using a complete or reconstructable 2 x 2 table. Main outcome measures Percentage reduction in uncertainty and corresponding information gain in bits; risk after positive and negative classifications; the proportion of information from each classification; and whether the upper 95% confidence bound for post-negative risk supported specified thresholds. Results The analysis included 482 evaluations of 407 calculators. The median study size was 422 and the median observed outcome frequency was 17.6%. Results reduced uncertainty by a median 14.9% (interquartile range 6.2%-30.5%; 95% confidence interval 12.1% to 17.2%), corresponding to 0.093 bits. In 329 evaluations (68.3%), one classification supplied more than 60% of average information. PERC reduced uncertainty by 4.3% on average while its negative classification lowered observed risk from 7.6% to 1.0%. Two HEART thresholds in the same cohort shifted the positive-classification information share from 39.1% to 79.8%. Post-negative risk was below 2% in 157 evaluations by point estimate, but the upper 95% confidence bound was below 2% in only 67; 90 of 157 (57%) did not support the apparent threshold. Conclusions Clinical calculator results have no single quantitative meaning. Conventional performance, information added beyond the observed outcome frequency, post-result risk, classification-specific information, and precision provide complementary interpretations. Whether acting on a result improves care remains a separate question.

Pulmonary embolism (PE) is the third leading cause of cardiovascular death worldwide, with a reported in-hospital all-cause mortality of over 25%.1,2 As patients with PE may present with a wide spe...

Aims: Acute intermediate-high-risk pulmonary embolism (PE) causes a pathological increase in pulmonary artery pressure and right ventricle afterload that may progress to right ventricle failure and cardiopulmonary collapse. Patients may require mechanical ventilation, further increasing pulmonary vascular resistance (PVR) and artery pressure. We aimed to investigate the ability of ventilator settings adjustments to reduce pulmonary artery pressure in a porcine model. Methods and results: Eleven Danish female pigs (Landrace/Yorkshire/Duroc) of ≈60 kg were used to perform a randomized, blinded, cross-over, experimental study. Following induction of acute PE, the animals were subject to four randomized interventions with wash-out periods in between: (1) changes in positive end-expiratory pressure (PEEP) (from 5 to 0, 10 and 15 cmH2O), (2) 50 and 100% increase in minute ventilation (hypocapnia), (3) increase in fraction of inspired oxygen (FiO2, from 21 to 40%), and (4) infusion of sodium bicarbonate to induce alkalosis. The main outcome was mean pulmonary artery pressure (mPAP). mPAP was reduced by a reduction in PEEP (28 ± 6 vs. 26 ± 5 mmHg, P = 0.011), hypocapnia (27 ± 6 vs. 23 ± 5 mmHg, P = 0.0004), alkalosis (27 ± 4 vs. 25 ± 5 mmHg, P = 0.003), and increased fraction of inspired oxygen (28 ± 6 vs. 23 ± 5 mmHg, P < 0.0001). Changes in PVR showed similar patterns (P < 0.05 for all). Conclusion: In a porcine model of acute PE reduction of PEEP, permissive hypocapnia through hyperventilation, alkalosis, and increased fraction of inspired oxygen can reduce mPAP.