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Record W2492715225 · doi:10.1093/ajcp/aqw087

Process Optimization to Improve Immunosuppressant Drug Testing Turnaround Time

2016· article· en· W2492715225 on OpenAlexaff
Vilte Barakauskas, Tiffany Bradshaw, Lonnie Smith, Christopher M. Lehman, Kamisha L. Johnson‐Davis

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2016
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsChildren's & Women's Health Centre of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsTurnaround timeProcess (computing)ScheduleMedicineComputer scienceExecution timeOperations managementReliability engineeringEngineeringOperating system

Abstract

fetched live from OpenAlex

OBJECTIVES: Timely reporting of immunosuppressant (ISP) drug level results is needed for transplant patient management. This study characterized the local ISP testing process, identified bottlenecks and implemented process improvements to meet turnaround time requirements. METHODS: Laboratory information time stamps, direct observation and discussion with staff were used to construct a value stream map of the ISP testing process to identify process bottlenecks. Improvements were implemented to attain the required turnaround time. RESULTS: Baseline performance of the existing ISP process (seven weeks, n = 272 samples) indicated that only 28% of samples were reported by 2:00 pm Major bottlenecks were identified to be the analytical run schedule, instrument delays, difficulty identifying ISP samples at intake, and difficulty collecting specimens. Process changes resulted in a median of 76% samples reported by 2:00 pm CONCLUSIONS: : Adjusting ISP collection and analysis processes improved the laboratory's ability to meet physician requested result reporting time of 2:00 pm.

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)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.417
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.033
GPT teacher head0.388
Teacher spread0.356 · 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.

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

Citations6
Published2016
Admission routes1
Has abstractyes

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