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Record W4293537885 · doi:10.2147/rru.s372208

Alpha-Blocker Prescribing Trends for Ureteral Stones: A Single-Centre Study

2022· article· en· W4293537885 on OpenAlexaff
Liang G. Qu, Garson Chan, Johan Gani

Bibliographic record

VenueResearch and Reports in Urology · 2022
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsTamsulosinMedicineMedical prescriptionEmergency departmentUrologyInternal medicinePharmacology

Abstract

fetched live from OpenAlex

Purpose: Recommendations for alpha-blockers have shifted in the conservative management of ureteral stones. It is unknown whether real-life practices regarding alpha-blocker prescriptions reflect updates in evidence. This study aimed to characterise alpha-blocker prescriptions for conservatively managed ureteral stones and relate this to recent literature. Methods: This was a retrospective audit, 01/01/2014 to 01/01/2019, of emergency acute renal colic presentations. Patients were included if they had a confirmed ureteral stone and were conservatively managed. The rates of alpha-blocker prescriptions were analysed using interrupted time-series analyses. May 2015 was selected as the cut-point to analyse before and after trend lines. Results were stratified by stone size and location. Tamsulosin and prazosin prescriptions were also compared. Results: This study included 2163 presentations: 70.4% were stones ≤5 mm and 61.4% were proximal stones. Altogether, 24.7% of presentations were prescribed alpha-blockers. There was a fall in alpha-blocker prescription rates from before to after May 2015, regardless of stone size or location (p < 0.001). Since May 2015, however, there was a monthly rate increase of 0.5% for patients with stones >5mm. Conclusion: This study demonstrated a significant shift in rates of alpha-blocker prescriptions, possibly related to the influence of updates in available high-quality evidence.

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)0.000
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 score0.125
Threshold uncertainty score0.450

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.098
GPT teacher head0.388
Teacher spread0.291 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2022
Admission routes1
Has abstractyes

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