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Record W4311758290 · doi:10.1016/j.euros.2022.11.014

A Systematic Review of Heterogeneity in Outcome Definition and Reporting in Localised Renal Cancer

2022· review· en· W4311758290 on OpenAlexaff
Katharina Beyer, Christiaan Widdershoven, Lisa M. Wintner, Saeed Dabestani, Lorenzo Marconi, Charlotte Moss, Netty Kinsella, Yuhong Yuan, Rachel Giles, Ravi Barod, Mieke Van Hemelrijck, Axel Bex, Patricia Zondervan, Steven MacLennan

Bibliographic record

VenueEuropean Urology Open Science · 2022
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineObservational studySystematic reviewMEDLINECohortRandomized controlled trialMeta-analysisRenal cell carcinomaStudy heterogeneityCohort studyIntensive care medicineFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Context: Outcomes in renal cell carcinoma (RCC) are reported inconsistently, with variability in definitions and measurement. Hence, it is difficult to compare intervention effectiveness and synthesise outcomes for systematic reviews and to create clinical practice guidelines. This uncertainty in the evidence makes it difficult to guide patient-clinician decision-making. One solution is a core outcome set (COS): an agreed minimum set of outcomes. Objective: To describe outcome reporting, definitions, and measurement heterogeneity as the first stage in co-creating a COS for localised renal cancer. Evidence acquisition: We systematically reviewed outcome reporting heterogeneity in effectiveness trials and observational studies in localised RCC. In total, 2822 studies (randomised controlled trials, cohort studies, case-control studies, systematic reviews) up to June 2020 meeting our inclusion criteria were identified. Abstracts and full texts were screened independently by two reviewers; in cases of disagreement, a third reviewer arbitrated. Data extractions were double-checked. Evidence synthesis: We included 149 studies and found that there was inconsistency in which outcomes were reported across studies and variability in the definitions used for outcomes that were conceptually the same. We structured our analysis using the outcome classification taxonomy proposed by Dodd et al. Outcomes linked to adverse events (eg, bleeding, outcomes linked to surgery) and renal injury outcomes (reduced renal function) were reported most commonly. Outcomes related to deaths from any cause and from cancer were reported in 44% and 25% of studies, respectively, although the time point for measurement and the analysis methods were inconsistent. Outcomes linked to life impact (eg, global quality of life) were reported least often. Clinician-reported outcomes are more frequently reported than patient-reported outcomes in the renal cancer literature. Conclusions: This systematic review underscores the heterogeneity of outcome reporting, definitions, and measurement in research on localised renal cancer. It catalogues the variety of outcomes and serves as a first step towards the development of a COS for localised renal cancer. Patient summary: We reviewed studies on localised kidney cancer and found that multiple terms and definitions have been used to describe outcomes. These are not defined consistently, and often not defined at all. Our review is the first phase in developing a core outcome set to allow better comparisons of studies to improve medical care.

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

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)0.013
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.017
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.278
GPT teacher head0.436
Teacher spread0.159 · 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

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations7
Published2022
Admission routes1
Has abstractyes

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