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Record W3006693901 · doi:10.1136/bmjoq-2020-000929

Reducing overutilisation of serum vitamin D testing at a tertiary care centre

2020· article· en· W3006693901 on OpenAlexaff
Felicia Tai, Ian Chin‐Yee, Alan Gob, Vipin Bhayana, Angela C. Rutledge

Bibliographic record

VenueBMJ Open Quality · 2020
Typearticle
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsSt Joseph's Health CareLondon Health Sciences CentreWestern University
Fundersnot available
KeywordsMedicineVitamin D and neurologyAuditVitaminTertiary careTest (biology)Internal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Testing of 25-hydroxy (25-OH) vitamin D serum levels has increased drastically in recent years and much of it is considered inappropriate based on current guidelines. METHODS: In consultation with our physician groups (experts and frequent orderers), we modified existing guidelines and implemented a rational policy for 25-OH vitamin D testing and 1,25 dihydroxy (1,25 di-OH) vitamin D testing at a tertiary care centre. A computer decision support tool requiring selection of one of five acceptable testing indications was created for each test as part of a computerised physician order entry system. RESULTS: As a result of our intervention, we observed a 27% decrease in the average monthly test volume for 25-OH vitamin D from 504±62 (mean±SD) tests per month to 370±33 (p<0.001). 1,25 di-OH vitamin D testing decreased 58% from 71±18 to 30±10 (p<0.001). The departments ordering the tests were similar during the preintervention and postintervention periods, and further audits, patient chart reviews and individualised physician feedback were required to ensure appropriate ordering of 1,25 di-OH vitamin D. The most common ordering reasons selected were malabsorption/dietary concerns (46%) for 25-OH vitamin D and renal failure (42%) for 1,25 di-OH vitamin D. CONCLUSIONS: Limitations of our computer decision support tool include a dependence on an honour system in selecting the testing indication and an inability to limit ordering frequency. Periodic monitoring of test volumes will be required to ensure adherence to guidelines. Despite these limitations, we have improved appropriate utilisation of these tests and reduced costs by approximately $C60 375 per year.

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)0.001
metaresearch head score (Gemma)0.013
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.188
GPT teacher head0.449
Teacher spread0.261 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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