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Record W2591082578 · doi:10.1097/pts.0000000000000363

Essential and Nonessential Blood Testing in the Clinical Teaching Unit

2017· article· en· W2591082578 on OpenAlexaff
C.N. Sherren, Andrew G. Day, Roy Ilan

Bibliographic record

VenueJournal of Patient Safety · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsKingston General HospitalQueen's University
Fundersnot available
KeywordsUnit (ring theory)MedicineComputer scienceMedical educationPsychologyMathematics education

Abstract

fetched live from OpenAlex

OBJECTIVES: The aim of the study was to evaluate the essential and nonessential blood tests ordered on the internal medicine clinical teaching units (CTUs) at Kingston General Hospital. Our aim was to establish a baseline performance measure identifying appropriate use of laboratory tests that could be used to inform improvement over time. METHODS: For an 8-week period, 14 CTU attending physicians at Kingston General Hospital were surveyed. They were asked for each of their patients, "What blood tests do you consider to be essential for tomorrow morning to maintain appropriate care for this patient?" The following day, blood tests that were ordered were compared with the "essential" list previously given by the attending physicians. RESULTS: Of 291 processed blood tests, 148 (51%) had not been considered essential by attending physicians; of the 203 tests considered essential, 60 (30%) were not ordered. Total agreement between "essential" and processed tests was poor (κ = 0.51; confidence interval, 0.45-0.56). CONCLUSIONS: Inadequate use of blood tests for CTU patients is common. Quality improvement initiatives should aim to address the lack of observed consensus between attending physicians' views and the ordered tests and to streamline decision-making and the ordering/communication processes. Clinical standards and guidelines regarding ordering of laboratory tests should be clearly defined.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.036
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.012
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 science0.0000.000
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 head0.112
GPT teacher head0.447
Teacher spread0.336 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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