MétaCan
Menu
Back to cohort
Record W2074621296 · doi:10.1089/end.2012.0063

Renal Stone Disease in Spinal-Cord–Injured Patients

2012· review· en· W2074621296 on OpenAlexaff
Blayne Welk, Andrew Fuller, Hassan Razvi, John D. Denstedt

Bibliographic record

VenueJournal of Endourology · 2012
Typereview
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsSt Joseph's Health CareWestern University
Fundersnot available
KeywordsMedicinePercutaneous nephrolithotomyUreteroscopySpinal cord injurySurgeryUrinary systemBladder stonesKidney stonesDiseasePopulationPercutaneousUreterInternal medicineSpinal cord

Abstract

fetched live from OpenAlex

Renal stone disease is common among patients with spinal cord injury (SCI). They frequently have recurrent stones, staghorn calculi, and bilateral stone disease. The potential risk factors for stones in the SCI population are lesion level, bladder management strategy, specific metabolic changes, and frequent urinary tract infections. There has been a reduction in struvite stones among these patients, likely as a result of advances in their urologic care. The clinical presentation of stone disease in patients with SCI may involve frequent urinary infections or urosepsis, and at the time of presentation patients may need emergency renal drainage. The proportion of patients who have their stones treated with different modalities is largely unknown. Shockwave lithotripsy (SWL) is commonly used to manage stones in patients with SCI, and there have been reports of stone-free rates of 50% to 70%. The literature suggests that the morbidity associated with percutaneous nephrolithotomy in these patients is considerable. Ureteroscopy is a common modality used in the general population to treat patients with upper tract stone disease. Traditional limitations of this procedure in patients with SCI have likely been overcome with new flexible scopes; however, the medical literature has not specifically reported on its use among patients with SCI.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.061
GPT teacher head0.381
Teacher spread0.320 · 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.

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

Citations52
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueJournal of EndourologySame topicKidney Stones and Urolithiasis TreatmentsFrench-language works237,207