Re: Concordance of Near Infrared Spectroscopy With Pressure Flow Studies in Men With Lower Urinary Tract Symptoms
Bibliographic record
Abstract
No AccessJournal of UrologyLetter to the Editor/Errata1 Jan 2012Re: Concordance of Near Infrared Spectroscopy With Pressure Flow Studies in Men With Lower Urinary Tract SymptomsD. E. Chung, R. K. Lee, S. A. Kaplan and A. E. Te J Urol 2010; 184: 2434–2439 Babak Shadgan, Andrew Macnab, and Lynn Stothers Babak ShadganBabak Shadgan More articles by this author , Andrew MacnabAndrew Macnab More articles by this author , and Lynn StothersLynn Stothers More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2011.09.022AboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail References 1 : Near-infrared spectroscopy (NIRS) for application in urology. J Urol2009; 181: 601. abstract 1667. Link, Google Scholar 2 : Near-infrared spectroscopy (NIRS): validation of bladder-outlet obstruction assessment using non-invasive parameters. Can J Urol2008; 15: 4241. Google Scholar 3 : Near-infrared spectroscopy: a novel, noninvasive, diagnostic method for detrusor overactivity in patients with overactive bladder symptoms—a preliminary and experimental study. Eur Urol2011; 59: 757. Google Scholar 4 : Near-infrared spectroscopy of the bladder: new parameters for evaluating voiding dysfunction. Int J Spect2011; 2011. article 814179. Google Scholar 5 : Near-infrared spectroscopy of the bladder: a new technique for studying lower urinary tract function in health and disease. Proc SPIE2010; 7548U10.1117/12.841066. Crossref, Google Scholar 6 : Re: Classification of male lower urinary tract symptoms using mathematical modelling and a regression tree algorithm of noninvasive near-infrared spectroscopy parameters. Eur Urol2010; 57: 332. Google Scholar 7 : Measurement of detrusor tissue saturation index (TSI) and comparison of detrusor oxygenation during urodynamics to natural filling/emptying using wireless near-infrared spectroscopy in the neurogenic bladder. J Urol2011; 185: e866. abstract 2164. Link, Google Scholar 8 : Progress of near-infrared spectroscopy and topography for brain and muscle clinical applications. J Biomed Opt2007; 12. 062104. Google Scholar 9 : Near-infrared spectroscopy/imaging for monitoring muscle oxygenation and oxidative metabolism in healthy and diseased humans. J Biomed Opt2007; 12. 062105. Google Scholar 10 : Classification of male lower urinary tract symptoms using mathematical modelling and a regression tree algorithm of noninvasive near-infrared spectroscopy parameters. Eur Urol2010; 57: 327. Google Scholar 11 : Algorithm construction methodology for diagnostic classification of near-infrared spectroscopy data. Spectroscopy2011; 25: 1. Google Scholar 12 : Development of a near-infrared spectroscopy instrument for applications in urology. Can J Urol2008; 15: 4233. Google Scholar Near Infrared Spectroscopy Study Group, Bladder Care Centre, University of British Columbia Hospital, Vancouver, British Columbia, Canada© 2012 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 187Issue 1January 2012Page: 359-361 Advertisement Copyright & Permissions© 2012 by American Urological Association Education and Research, Inc.MetricsAuthor Information Babak Shadgan More articles by this author Andrew Macnab More articles by this author Lynn Stothers More articles by this author Expand All Advertisement PDF downloadLoading ...
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".