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Orthopedic Implant Value Drivers: A Qualitative Survey Study of Hospital Purchasing Administrators

2015· article· en· W2280242872 on OpenAlexaff
Chuan Silvia Li, Christopher Vannabouathong, Sheila Sprague, Mohit Bhandari

Bibliographic record

VenueJournal of Long-Term Effects of Medical Implants · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsHamilton General HospitalMcMaster University Medical CentreMcMaster University
Fundersnot available
KeywordsPurchasingAffect (linguistics)Qualitative researchPopulationMedicineMarketingHealth careBusinessPsychologyEnvironmental healthPolitical scienceSociology

Abstract

fetched live from OpenAlex

Osteoarthritis (OA) is a chronic, degenerative disease that is highly prevalent in the population, yet the factors that affect purchasing decisions related to this condition are poorly understood. A questionnaire was developed and administered to hospital executives across North America to determine the factors that affect purchasing decisions related to OA. Thirty-four individuals participated in the survey. Clinical evidence and cost effectiveness were deemed to be the most important factors in the process of making purchasing decisions. The most important considerations for adopting new technology were whether there was sufficient evidence in the literature, followed by thoughts of key opinion leaders, and cost of intervention/device. Ongoing research is still needed, but the current study allowed us to identify some trends in the data, providing new insight on how hospital purchasing decisions are made, which could have an immediate impact on those currently involved with making these decisions.

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)0.047
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0470.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.293
GPT teacher head0.491
Teacher spread0.198 · 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
Published2015
Admission routes1
Has abstractyes

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