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Repeat Imaging to Avoid Surgery: An Initiative to Reduce-Negative Ureteroscopy in Patients with Ureteral Stones

2022· article· en· W4281662341 on OpenAlexaff
Callum Lavoie, Max Levine, Timothy A. Wollin, Trevor Schuler, Shubha De

Bibliographic record

VenueJournal of Urological Surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicUreteral procedures and complications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineUreteroscopySurgeryGeneral surgeryUreter

Abstract

fetched live from OpenAlex

Negative ureteroscopy is a clinical occurrence defined by ureteroscopy being performed for a ureteric or renal calculus identified on radiographic imaging pre-operatively, with no calculus ultimately being identified intra-operatively due to the calculus being passed prior to the procedure being performed. This occurrence has been reported in the existing literature with rates between 6.3 and 9.8% in cohorts consisting of patients with both ureteric and renal calculi. The rates of negative ureteroscopy can be reduced by having more timely operative intervention, and by utilizing repeat pre-operative imaging when indicated. Smaller and more distal calculi are more likely to pass prior to intervention and result in a negative ureteroscopy. This study provides further information regarding the rates of negative ureteroscopy in a specific cohort of patients with only ureteric calculi. These patients did have a higher rate of negative ureteroscopy than other populations in the literature. Additionally, patients who were more symptomatic from their stone undergoing more expedited intervention were actually more likely to have passed their stone prior to intervention when compared to patients undergoing delayed intervention. This highlights the need for repeat imaging when available prior to intervention.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.035
GPT teacher head0.284
Teacher spread0.249 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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