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Record W2163187222 · doi:10.5858/2002-126-0809-cwmoyd

Continuous Wristband Monitoring Over 2 Years Decreases Identification Errors

2002· article· en· W2163187222 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueArchives of Pathology & Laboratory Medicine · 2002
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)MedicineContext (archaeology)Physical therapyPhlebotomySurgery

Abstract

fetched live from OpenAlex

CONTEXT: Identification of patients is one of the first steps in ensuring the accuracy of laboratory results. In the United States, hospitalized patients wear wristbands to aid in their identification, but wristbands errors are frequently found. OBJECTIVE: To investigate if continuous monitoring of wristband errors by participants of the College of American Pathologists (CAP) Q-Tracks program results in lower wristband error rates. SETTING: A total of 217 institutions voluntarily participating in the CAP Q-Tracks interlaboratory quality improvement program in 1999 and 2000. DESIGN: Participants completed a demographic form, answered a questionnaire, collected wristband data, and at the end of the year, best and most improved performers answered another questionnaire seeking suggestions for improvement. Each institution's phlebotomists inspected wristbands for errors before performing phlebotomy and recorded the number of patients with wristband errors. On a monthly basis, participants submitted data to the CAP for data processing, and at the end of each quarter, participants received summarized comparisons. At the end of each year, participants also received a critique of the results along with suggestions for improvement. MAIN OUTCOME MEASURES: The percentage of wristband errors by quarter, types of wristband errors, and suggestions for improvement. RESULTS: During 2 years, 1 757 730 wristbands were examined, and 45 197 wristband errors were found. The participants' mean wristband error rate for the first quarter in 1999 was 7.40%; by the eighth quarter, the mean wristband error rate had fallen to 3.05% (P <.001). Continuous improvement occurred in each quarter for participants in the 1999 and 2000 program and in 7 of 8 quarters for those who participated in both 1999 and 2000. Missing wristbands accounted for 71.6% of wristband errors, and best performers usually had wristband error rates under 1.0%. The suggestion for improvement provided by the largest number of best and most improved performers was that phlebotomists should refuse to perform phlebotomy on a patient when a wristband error is detected. CONCLUSIONS: The wristband error rate decreased markedly when this rate was monitored continuously using the CAP Q-Tracks program. The Q-Tracks program provides a useful tool for improving the quality of services in anatomic pathology and laboratory medicine.

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.

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)0.003
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 score0.505
Threshold uncertainty score0.715

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.043
GPT teacher head0.351
Teacher spread0.308 · 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