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Classification and standardized reporting of percutaneous nephrolithotomy (PCNL): International Alliance of Urolithiasis (IAU) Consensus Statements

2022· article· en· W4220786936 on OpenAlexaff
Simon Choong, Jean de la Rosette, John D. Denstedt, Guohua Zeng, Kemal Sarıca, Giorgio Mazzon, Iliya Saltirov, Shashi Kiran Pal, Madhu Agrawal, Janak Desai, Aleš Petřík, Noor Buchholz, Marcus V. Maroclo, Stephen B. Gordon, Ashwin Sridhar

Bibliographic record

VenueMinerva Urology and Nephrology · 2022
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineAllianceMEDLINEPercutaneous nephrolithotomyStatement (logic)Outcome (game theory)Medical physicsSurgeryPercutaneousPolitical scienceMathematics

Abstract

fetched live from OpenAlex

BACKGROUND: The aim of this study was to reach a consensus in the classification and standardized reporting for the different types of PCNLs.METHODS: The RAND/UCLA appropriateness methodology was used to reach a consensus. Thirty-two statements were formulated reviewing the literature on guidelines and consensus on PCNLs, and included procedure specific details, outcome measurements and a classification for PCNLs. Experts were invited to two rounds of input, the first enabled independent modifications of the proposed statements and provided the option to add statements. The second round facilitated scoring of all statements. Each statement was discussed in the third round to decide which statements to include. Any suggestion or disagreement was debated and discussed to reach a consensual agreement.RESULTS: Twenty-five recommendations were identified to provide standardized reporting of procedure and outcomes. Consensual scoring above 80% were strongly agreed upon by the panel. The top treatment related outcomes were size of sheath used (99.1%) and position for PCNL (93.5%). The highest ranked Outcome Measures included definition of postoperative hospital length of stay (94.4%) and estimated blood loss (93.5%).CONCLUSIONS: The consensus statements will be useful to clarify operative technique, in the design of clinical trials and standardized reporting, and presentation of results to compare outcomes of different types of PCNLs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Reporting · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.337
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Other design
DomainReporting
GenreMethods · Commentary

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".

Quick stats

Citations15
Published2022
Admission routes1
Has abstractyes

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