International Multi-Specialty Delphi Survey: Identification of Diagnostic Criteria for Hepatic and Renal Cyst Infection
Bibliographic record
Abstract
BACKGROUND: Cyst infection is one of the complications of autosomal dominant polycystic kidney disease and polycystic liver disease. The diagnosis is typically made on a mix of clinical, laboratory and imaging abnormalities but the importance of individual items is uncertain. We aimed to perform a Delphi survey amongst physicians to achieve consensus on diagnostic criteria. METHODS: We retrieved diagnostic items from the literature and conducted physician and patient interviews. All items were combined to create the online questionnaire. Participants rated each item during 3 consecutive rounds. Items were rated for diagnostic helpfulness for hepatic and renal cyst infection on a 9-point scale with anchors, from extremely unimportant (n = 1) to extremely important (n = 9). We determined consensus with the disagreement index. The median rating of each item was calculated and categorized into inappropriate (≤3.4), uncertain (3.5-6.4) or appropriate (≥6.5). By combining all items that reached an appropriate consensus rating, we developed a diagnostic algorithm based on expert consensus. RESULTS: We invited 58 physicians to participate in the survey. In total, 35 (60%) responded to round 1 of which 91% (n = 32) and 86% (n = 30) responded to round 2 and 3, respectively. The final panel included 23 nephrologists, 5 hepatologists, a nuclear medicine specialist and an infectious disease physician from 11 countries (male 67%, mean age 47 ± 11 years, median clinical experience 21 years). The panel rated the diagnostic helpfulness of 59 potential items. Ultimately, 22 hepatic and 26 renal items were rated appropriate, including positive blood cultures and fluorodeoxyglucose positron-emission CT imaging. Ultrasonography and absence of intracystic bleeding were amongst those deemed uncertain or inappropriate. Subsequently, by combining items rated appropriate, we developed a clinical tool to diagnose hepatic and renal cyst infection. CONCLUSIONS: We identified diagnostic items for hepatic and renal cyst infection and developed an expert-based diagnostic algorithm, which may aid physicians in the diagnostic work-up. A prospective study is necessary to validate this algorithm.
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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 | 0.047 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".