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Record W2800002034 · doi:10.1097/coc.0000000000000448

Mohs Micrographic Surgery Volume and Payment Patterns Among Dermatologists in the Medicare Population, 2013

2018· article· en· W2800002034 on OpenAlexaff
Cameron Johnstone, Keith A. Joiner, John B. Pierce, Robert S. Krouse

Bibliographic record

VenueAmerican Journal of Clinical Oncology · 2018
Typearticle
Languageen
FieldMedicine
TopicNonmelanoma Skin Cancer Studies
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsMedicineReimbursementMedicare Part BDecilePaymentPopulationFamily medicineHealth careFinanceEnvironmental healthStatistics

Abstract

fetched live from OpenAlex

OBJECTIVES: Mohs micrographic surgery (MMS) has expanded markedly in recent years but there is limited information on volume, practice patterns or reimbursement. This study characterizes MMS utilization in the Medicare population. MATERIALS AND METHODS: We analyzed the Medicare Provider Utilization and Payment Data: Physician and Other Supplier Public Use File Calendar Year 2013 data set for provider service volume and reimbursement for dermatologists who did and did not perform MMS procedures. RESULTS: Total Medicare-funded MMS procedures increased 25% from 2009 (558,447) to 2013 (700,262). Dermatologists who performed MMS had significantly more average services per provider (5419.4 vs. 3627.1, r=0.16, P<0.0001), were reimbursed significantly more in average total procedure-related compensation ($475,883.64 vs. $144,564.74, r=0.49, P<0.0001) than dermatologists who did not perform MMS, and made up 71.3% of the top decile of dermatologists ranked by total reimbursement received from Medicare. Total MMS service volume and reimbursement was concentrated among a subset of providers. Among MMS providers, a higher volume of MMS procedures was correlated with a greater likelihood of performing procedures on lesions located on the trunk, arms or legs (r=0.27, P<0.001). CONCLUSIONS: In 2013 reimbursement for MMS comprised almost 19% of the amount reimbursed by Medicare Part B Fee For Service to dermatologists and greater than half a percent of the total amount reimbursed to all physicians participating in the program. Further studies incorporating clinical and outcomes data are needed to evaluate appropriate utilization of this procedure.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.123
Threshold uncertainty score0.767

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.051
GPT teacher head0.415
Teacher spread0.364 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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