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Record W2142278657 · doi:10.1377/hlthaff.2010.0210

New Cardiac Surgery Programs Established From 1993 To 2004 Led To Little Increased Access, Substantial Duplication Of Services

2011· article· en· W2142278657 on OpenAlexaff
Frances Leslie Lucas, Andrea E. Siewers, David C. Goodman, Dongmei Wang, David E. Wennberg

Bibliographic record

VenueHealth Affairs · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsCentre for Advancing Health Outcomes
FundersNational Heart, Lung, and Blood Institute
KeywordsCertificateQuality (philosophy)Competition (biology)BusinessMedicineComputer science

Abstract

fetched live from OpenAlex

Despite decreasing demand for bypass surgery, 301 new cardiac surgery programs opened between 1993 and 2004. We used Medicare data to identify where the new programs opened and to assess their impact on access and efficiency. Forty-two percent of the new programs opened in communities that already had access to cardiac surgery, which suggests that their creation has led to a fight for shares of a shrinking market. New programs were much more likely to open in states that did not require them to show a certificate-of-need. Overall, travel time to the nearest cardiac surgery program changed little, which suggests that these programs have done little to improve geographic access. The duplication of services that resulted in many areas may have engendered competition based on quality, price, or both, but it may also have increased surgical rates, with unknown results. We observe that certificate-of-need requirements may help avoid unnecessary duplication of services by preventing new programs from opening in close proximity to existing ones.

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)0.002
metaresearch head score (Gemma)0.012
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 score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.068
GPT teacher head0.286
Teacher spread0.218 · 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".

Quick stats

Citations10
Published2011
Admission routes1
Has abstractyes

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