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Record W1780119848 · doi:10.1002/jhm.2292

Grade pending: Lessons for hospital quality reporting from the <scp>N</scp>ew <scp>Y</scp>ork <scp>C</scp>ity restaurant sanitation inspection program

2014· article· en· W1780119848 on OpenAlexaff
Andrew M. Ryan, Allan S. Detsky

Bibliographic record

VenueJournal of Hospital Medicine · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsUniversity of TorontoUniversity Health NetworkMount Sinai Hospital
FundersAgency for Healthcare Research and QualityUniversity of Oxford
KeywordsSanitationMedicineQuality (philosophy)Medical emergency

Abstract

fetched live from OpenAlex

Public quality reporting programs have been widely implemented in hospitals in an effort to improve quality and safety. One such program is Hospital Compare, Medicare's national quality reporting program for US hospitals. The New York City sanitary grade inspection program is a parallel effort for restaurants. The aims of Hospital Compare and the New York City sanitary inspection program are fundamentally similar: to address a common market failure resulting from consumers' lack of information on quality and safety. However, by displaying easily understandable information at the point of service, the New York City sanitary inspection program is better designed to encourage informed consumer decision making. We argue that this program holds important lessons for public quality reporting of US hospitals.

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)0.008
metaresearch head score (Gemma)0.054
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.054
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.098
GPT teacher head0.358
Teacher spread0.260 · 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.

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

Citations2
Published2014
Admission routes1
Has abstractyes

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