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Record W1504411180 · doi:10.1063/1.2723580

Update On Erbium:YAG lithotripsy

2007· article· en· W1504411180 on OpenAlexaff
Joel M.H. Teichman, Hyun Wook Kang, Randolph D. Glickman, Ashley J. Welch

Bibliographic record

VenueAIP conference proceedings · 2007
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsUniversity of British ColumbiaProvidence Health Care
Fundersnot available
KeywordsHolmiumLithotripsyLaser lithotripsyErbiumMaterials scienceLaserPhotothermal therapyOpticsOptoelectronicsMedicineSurgeryNanotechnologyPhysics

Abstract

fetched live from OpenAlex

The Holmium:YAG laser fragments stones by a photothermal mechanism. It produces tiny fragments compared to short pulse duration lasers which fragment stones by laser induced shockwave lithotripsy. Holmium:YAG lithotripsy fragments stones of all compositions, but fragments stones slowly. In an effort to achieve photothermal lithotripsy more efficiently, Erbium:YAG lithotripsy has been tested. The Erbium:YAG laser fragments stones up to 5 times more efficiently than the Holmium:YAG laser. Its principal limitation is that it is not transmitted well by currently available fibers. Erbium:YAG lithotripsy is more efficient than Holmium:YAG lithotripsy but it is not yet practical for clinical use with current technology.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.472
Threshold uncertainty score0.716

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)0.0010.001

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 head0.026
GPT teacher head0.279
Teacher spread0.254 · 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 designNot applicable
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

Citations5
Published2007
Admission routes1
Has abstractyes

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