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Record W2434288623 · doi:10.1097/coc.0000000000000307

Cost-effectiveness of Management Options for Small Renal Mass

2016· review· en· W2434288623 on OpenAlexaboutno aff
Ye Wang, Yu‐Wei Chen, Jeffrey J. Leow, Alison Levy, Steven L. Chang, Francisco-Hammerschmidt Gelpi

Bibliographic record

VenueAmerican Journal of Clinical Oncology · 2016
Typereview
Languageen
FieldMedicine
TopicRenal cell carcinoma treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineRenal massNephrectomyCost effectivenessSurgeryVariety (cybernetics)Intensive care medicineGeneral surgeryMedical physicsKidneyRisk analysis (engineering)Internal medicine

Abstract

fetched live from OpenAlex

Costs of surgery for small renal masses (SRMs) are high. This study aimed to systematically review and evaluate the cost-effectiveness analyses of management options for SRMs. Six databases were searched from inception to August 2015. Inclusion criteria were full original research, full economic evaluation of management options for SRM, and written in English. Among 776 studies screened, 6 met the inclusion criteria. Ablation was cost-effective versus nephron-sparing surgery. Laparoscopic partial nephrectomy was cost-effective versus the open approach. Renal mass biopsy dominated immediate treatment in the United States, but not in Canada. According to the Consolidated Health Economic Evaluation Reporting Standards, all the studies had relatively good quality. Despite the observed evidence, future research is needed to fill in the knowledge gap. A few suggestions should be kept in mind such as conducting the cost-effectiveness analysis in a variety of countries.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.986
Threshold uncertainty score0.932

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0080.003
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.313
GPT teacher head0.532
Teacher spread0.219 · 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 designOther design
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

Citations15
Published2016
Admission routes1
Has abstractyes

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