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Record W4313532104 · doi:10.1016/j.ajur.2022.11.003

Thulium fiber laser lithotripsy: Is it living up to the hype?

2022· review· en· W4313532104 on OpenAlex
John D. Denstedt, Fernanda Berto

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAsian journal of urology · 2022
Typereview
Languageen
FieldMedicine
TopicKidney Stones and Urolithiasis Treatments
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineLaser lithotripsyLithotripsyThuliumLaserYttrium aluminium garnetHolmiumFiber laserSurgeryOptics

Abstract

fetched live from OpenAlex

The holmium:yttrium-aluminium-garnet laser (Ho:YAG) has been the gold standard for laser lithotripsy over the last three decades. After demonstrating good in vitro efficacy, the thulium fiber laser (TFL) has been recently released in the market and the initial clinical results are encouraging. This article aims to review the main technology differences between the Ho:YAG laser and the TFL, discuss the initial clinical results with the TFL as well as the optimal settings for TFL lithotripsy. We reviewed the literature focusing on the technological aspects of the Ho:YAG laser and TFL as well as the results of in vitro and in vivo studies comparing both technologies. In vitro studies show a technical superiority of TFL compared to the Ho:YAG laser and encouraging results have been demonstrated in clinical practice. However, as TFL is a new technology, limited studies are currently available, and the optimal settings for lithotripsy are not yet established. TFL has the potential to be an alternative to the Ho:YAG laser, but more reports are still needed to determine the optimal laser for lithotripsy of urinary tract stones when considering all parameters including effectiveness, safety, and costs.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.846
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.065
GPT teacher head0.357
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