MétaCan
Menu
Back to cohort
Record W3157743721 · doi:10.1097/ju.0000000000001836

Natural History of Post-Treatment Kidney Stone Fragments: A Systematic Review and Meta-Analysis

2021· review· en· W3157743721 on OpenAlexaboutno aff
Eleanor Brain, Robert Geraghty, Catherine Lovegrove, Bingyuan Yang, Bhaskar Somani

Bibliographic record

VenueThe Journal of Urology · 2021
Typereview
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsnot available
FundersNational Institute for Health and Care Research
KeywordsMedicineMeta-analysisCochrane LibraryInternal medicinePublication biasPercutaneous nephrolithotomyAsymptomaticSurgeryPercutaneous

Abstract

fetched live from OpenAlex

PURPOSE: We assessed the literature around post-treatment asymptomatic residual stone fragments and performed a meta-analysis. The main outcomes were intervention rate and disease progression. MATERIALS AND METHODS: We searched Ovid®, MEDLINE®, Embase™, the Cochrane Library and ClinicalTrials.gov using search terms: "asymptomatic", "nephrolithiasis", "ESWL", "PCNL", "URS" and "intervention." Inclusion criteria were all studies with residual renal fragments following treatment (shock wave lithotripsy, ureteroscopy or percutaneous nephrolithotomy). Analysis was performed using 'metafor' in R and bias determined using Newcastle-Ottawa scale. RESULTS: =0.57, Cochran's Q=7.11 (p=0.07) and Egger's regression: z=-0.75, p=0.45. Bias analysis demonstrated a moderate risk. CONCLUSIONS: Larger post-treatment residual fragments are significantly more likely to require further intervention especially in the long term. Smaller fragments, although less likely to require further intervention, still carry that risk. Notably, there is no significant difference in disease progression between fragment sizes. Patients with residual fragments should be appropriately counselled and informed decision-making regarding further management should be done.

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)0.001
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: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.350
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0140.005
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 head0.062
GPT teacher head0.346
Teacher spread0.284 · 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 designMeta-analysis
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

Citations93
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueThe Journal of UrologySame topicKidney Stones and Urolithiasis TreatmentsFrench-language works237,207