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Record W2796011164 · doi:10.1016/j.juro.2018.02.2581

MP76-13 PROSTATE BIOPSY COSTS FOR PRIVATELY-INSURED MEN: IMPACT OF MAGNETIC RESONANCE IMAGE-GUIDANCE AND USE OF ANESTHESIA SERVICES

2018· article· en· W2796011164 on OpenAlexaff
Andrew Leung, Wen Liu, Dattatraya Patil, Mark Henry, David Howard, Heqiong Wang, Reneé Moore, Martin G. Sanda, Christopher P. Filson

Bibliographic record

VenueThe Journal of Urology · 2018
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsHealth Care Foundation
FundersWinship Cancer Institute
KeywordsMedicineProstate biopsyBiopsyMagnetic resonance imagingProstateSedationGeneral surgerySurgeryRadiologyCancerInternal medicine

Abstract

fetched live from OpenAlex

You have accessJournal of UrologyGeneral & Epidemiological Trends & Socioeconomics: Value of Care: Cost & Outcomes Measures I1 Apr 2018MP76-13 PROSTATE BIOPSY COSTS FOR PRIVATELY-INSURED MEN: IMPACT OF MAGNETIC RESONANCE IMAGE-GUIDANCE AND USE OF ANESTHESIA SERVICES Andrew Leung, Wen Liu, Dattatraya Patil, Mark Henry, David Howard, Heqiong Wang, Renee Moore, Martin Sanda, and Christopher Filson Andrew LeungAndrew Leung More articles by this author , Wen LiuWen Liu More articles by this author , Dattatraya PatilDattatraya Patil More articles by this author , Mark HenryMark Henry More articles by this author , David HowardDavid Howard More articles by this author , Heqiong WangHeqiong Wang More articles by this author , Renee MooreRenee Moore More articles by this author , Martin SandaMartin Sanda More articles by this author , and Christopher FilsonChristopher Filson More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.2581AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Prostate biopsy technique is evolving, with use of magnetic resonance imaging (MRI) guidance and anesthesia services becoming more common. We evaluated the cost impact of these changes among biopsies for privately-insured men in the United States. METHODS Using MarketScan Commercial Claims database, we identified biopsies performed from 2009-2015 with appropriate procedure codes. Pertinent payments were identified within a 21-day window surrounding each biopsy claim and 7-day window surrounding any MRI claim. We assigned MRI-guidance if pelvic MRI was done within 3 months prior to biopsy. Receipt of anesthesia (CPT 00100-01999) or sedation (CPT 99143-99145; 99148-99150) was identified. All pertinent pathology services were captured. Patient cost-sharing was based on copayments and deductibles. Overall and component median payments were identified and compared with appropriate parametric testing. RESULTS We identified 311,517 biopsies that were MRI-guided (n=6,888), ultrasound-guided (n=302,449), or transperineal (n=2,180). Over 14% of biopsies were with general anesthesia, more commonly if MRI-guided (22%) or transperineal (82%). Median payments were highest for MRI-guided biopsies ($4356, IQR $2699-7153), followed by transperineal ($2671, IQR 1793-4213) and ultrasound-guided biopsies ($1842, IQR $1274-2600) (p<0.001) (Figure). Patient cost sharing was also greater with MRI-guided biopsies ($346 vs $180 ultrasound-guided, p<0.001) and transperineal biopsies ($255 vs $180, p<0.001). For MRI-guided biopsies, median imaging costs were significantly greater ($1417 vs $294 ultrasound-guided, p<0.001). Median payments for anesthesia services were $528 (IQR $240-892), $270 (IQR $60-464), and $503 (IQR $394-682) for MRI-guided, ultrasound-guided, and transperineal biopsies, respectively. CONCLUSIONS Median payments for MRI-guided biopsies were over twice as great as those for traditional ultrasound-guided biopsies, with the differences in payments being driven mainly by imaging costs. These findings have important implications as efforts focus on delivery of high-value services used for prostate cancer detection. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e1023 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Andrew Leung More articles by this author Wen Liu More articles by this author Dattatraya Patil More articles by this author Mark Henry More articles by this author David Howard More articles by this author Heqiong Wang More articles by this author Renee Moore More articles by this author Martin Sanda More articles by this author Christopher Filson More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0200.002

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 head0.292
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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