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Record W2010895780 · doi:10.1097/pap.0b013e31826661b7

Directed Peer Review in Surgical Pathology

2012· review· en· W2010895780 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvances in Anatomic Pathology · 2012
Typereview
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsComputer scienceQuality assuranceSurgical pathologyRoot cause analysisMedicineMedical physicsPathologyReliability engineering

Abstract

fetched live from OpenAlex

Second pathologist peer review is used in many surgical laboratory quality-assurance programs to detect error. Directed peer review is 1 method of second review and involves the selection of specific case types, such as cases from a particular site of anatomic origin. The benefits of using the directed peer review method are unique and directed peer review detects both errors in diagnostic accuracy and precision and this detection may be used to improve practice. We utilize the Lean quality improvement A3 method of problem solving to investigate these issues. The A3 method defines surgical pathology diagnostic error and describes the current state in surgical pathology, performs root cause analysis, hypothesizes an ideal state, and provides opportunities for improvement in error reduction. Published data indicate that directed peer review practices may be used to prevent active cognitive errors that lead to patient harm. Pathologists also may use directed peer review data to target latent factors that contribute to error and improve diagnostic precision.

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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.974
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
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 head0.108
GPT teacher head0.490
Teacher spread0.382 · 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