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Record W2084295596 · doi:10.1309/fyn4-lpy0-42hn-9f4q

Estimating the Budgetary Impact of Setting the Medicare Clinical Laboratory Fee Schedule at the National Limitation Amount

2002· article· en· W2084295596 on OpenAlexaboutno aff
Ronald L. Weiss, David N. Sundwall, John M. Matsen

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2002
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsnot available
Fundersnot available
KeywordsTest (biology)SchedulePaymentActuarial scienceFactoringQuarter (Canadian coin)BusinessMedicineEconomicsFinanceGeography

Abstract

fetched live from OpenAlex

The Institute of Medicine (IOM) of the National Academy of Sciences was commissioned by Congress to study the current system for the payment of diagnostic clinical laboratory services provided to Medicare beneficiaries. The current system was established in 1984 and has grown in complexity and is of diminishing contemporary relevance. The IOM recommended that a single, rational, nationalfee schedule be established and that it be initially based on the National Limitation Amount (NLA) currently mandated as the national fee cap. To estimate the potential budgetary impact of this recommendation, we merged the 1999 Part B Extract and Summary System and the 1999 Clinical Diagnostic Laboratory Fee Schedule (CLFS). By using an estimated 193 million allowed services from this data set and the current mean fee of $9.14 per test, current spending is approximately $1,768 million. The impact of raising the CLFS to the NLA will be approximately $1,792 million, or $9.26 per test. The estimated cumulative budgetary effect, factoring in the current forecast for the Consumer Price Index, is an increase of approximately $993 million over 5 years and $2,359 million over 10 years.

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)0.081
metaresearch head score (Gemma)0.068
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0810.068
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.400
GPT teacher head0.532
Teacher spread0.133 · 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; both teacher heads agree on what is shown here.

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

Citations3
Published2002
Admission routes1
Has abstractyes

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