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Record W4296886258 · doi:10.21649/akemu.v28i2.5082

Predictability of Computerized Tomography as Compared to Ureteroscopy in Detection of Ureteric Stone in Patients with Indwelling Stents

2022· article· en· W4296886258 on OpenAlexaff
Nawaz Rashid, Zia-ur -Rehman, Iqbal Hussain Dogar, Kamran Sajjad Hashmi, Fareeha Nawaz

Bibliographic record

VenueAnnals of King Edward Medical University · 2022
Typearticle
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsContinental (Canada)
Fundersnot available
KeywordsUreteroscopyMedicineExtracorporeal shock wave lithotripsyGold standard (test)RadiologyPredictive valueStentUreterLithotripsySurgeryInternal medicine

Abstract

fetched live from OpenAlex

Objective: The current gold standard for the diagnosis of ureteric stones in patients with a stent in situ is ureteroscopy but this study is planned to determine the positive predictive value of computerized tomography (CT) scan among such patients. Methods: This study involved patients who had ureteral stent in situ and were referred for re-evaluation of residual stones after extracorporeal shock wave lithotripsy. These patients underwent CT-scan for detection of ureteric stone. Later on, ureteroscopy was performed and ureteric stone was confirmed on direct visualization. Results: The mean age of the patients was 32.2±8.9 years. Male to female ratio of 1.7:1. CT scan shows a stone in 252 patients (70.2%) out of which 165 (46.0%) were confirmed on ureteroscopy. This yielded a sensitivity of 88.7 %, specificity of 49.7 %, positive predictive value of 65.5%, negative predictive value of 80.4% and diagnostic accuracy of 69.9% of CT for detecting ureteric calculi in patients with ureteric stents (p value < 0.0001). Conclusion: CT scan owing to its limited diagnostic accuracy cannot replace ureteroscopy for detection of ureteric stones in patients with ureteric stents.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.260
Teacher spread0.244 · 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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