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Record W2777371180 · doi:10.1093/ajcp/aqx145

Assessment of the Measurement Error in Cyclosporine Levels Drawn Between Peripheral and Central Sources

2017· article· en· W2777371180 on OpenAlexaff
Andrew W. Shih, Mark Crowther, Erin Jamula, Rami T. El–Sharkawy, Mark D. Brown, Georgina Paterson, Michelle Lui, Andrew Don-Wauchope

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2017
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsJuravinski Cancer CentreMcMaster UniversityUniversity of British Columbia
Fundersnot available
KeywordsPeripheralMedicineCatheterCentral venous catheterPeripheral bloodLumen (anatomy)TransplantationSurgeryUrologyAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVES: Cyclosporine is often monitored by drug levels drawn through central venous catheters (CVCs), which may be falsely elevated due to reversible drug adsorption onto the catheter. Therefore, we assessed the correlation between cyclosporine levels drawn peripherally and through CVCs. METHODS: Bone marrow transplantation patients had a weekly collection of both peripheral and CVC draws from dual-lumen catheters simultaneously to assess cyclosporine levels after research ethics approval. Our primary outcome was the proportion of paired samples that were incongruent-defined as the mean of the CVC level being greater than 2 standard deviations from the peripheral level mean. RESULTS: After approaching 27 eligible patients, 20 patients (77.8%) provided samples. Of 53 paired samples, seven were incongruent (13.2%). Peripheral and CVC levels correlated (r = 0.91) and agreed well. CONCLUSION: Despite potential for preanalytical error due to adsorption, cyclosporine infusion and monitoring via CVCs produce results similar to monitoring via peripheral blood draws.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.078
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.033
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0330.078
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.132
GPT teacher head0.456
Teacher spread0.324 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2017
Admission routes1
Has abstractyes

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Same venueAmerican Journal of Clinical PathologySame topicRenal Transplantation Outcomes and TreatmentsFrench-language works237,207