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Record W2593162419 · doi:10.1373/jalm.2016.021725

Instrument Error Codes and Diagnostic Serendipity

2017· article· en· W2593162419 on OpenAlexaff
Andrew W. Lyon, Harvey La Rocque, Simone Corriveau, Robert D. Saunders

Bibliographic record

VenueThe Journal of Applied Laboratory Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicRenal function and acid-base balance
Canadian institutionsUniversity of SaskatchewanSaskatchewan Health Authority
Fundersnot available
KeywordsMedicineAnemiaComplete blood countComputer scienceAlgorithmInternal medicine

Abstract

fetched live from OpenAlex

A 60-year-old female patient presented to a family physician with recent symptoms of fatigue, but no prior medical problems. During that initial visit, the physician ordered laboratory evaluations for anemia (complete blood count, iron, and vitamin B12), thyroid status, and renal function (Table 1). The ordered test results were not remarkable except for an unexplained mild anemia: hemoglobin 9.1 g/dL (91 g/L); hyponatremia: Na 127 mmol/L; and hypoalbuminemia: albumin 2.1 g/dL (21 g/L). View this table: Table 1. Selected results of laboratory testing performed after initial evaluation of the patient with age- and sex-dependent reference intervals.a Results for vitamin B12 could not be provided by the laboratory. During the analysis, a series of instrument error codes were observed by staff and the instrument stopped. The error codes were triggered after incubation of plasma with reagents for measurement of vitamin B12 on an E601 analyzer (Roche Diagnostics). The error codes appeared in the software and computer that operates the E601 analyzer and were recorded by the middleware (Cobas I.T. middleware 1.05.02) but was not communicated to the laboratory information system). The 4 error codes indicated a shortage of auxiliary reagent, an abnormal measuring cell condition, an abnormal sipper pipette movement, and a tip/cup pickup error. During investigation, staff observed that as an automated pipette …

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
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 head0.021
GPT teacher head0.286
Teacher spread0.265 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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