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Record W2754747763 · doi:10.1136/jclinpath-2017-204731

Standardisation of practice for Canadian pathologists’ assistants

2017· editorial· en· W2754747763 on OpenAlexaffabout
Martin Grealish, Alan Wolff, J B Eastwood, Danielle Lee

Bibliographic record

VenueJournal of Clinical Pathology · 2017
Typeeditorial
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsCanadian Association of PhysicistsUniversity Health Network
Fundersnot available
KeywordsQuality assuranceSurgical pathologyMedicineMedical physicsPathologyGood laboratory practiceComputer scienceExternal quality assessment

Abstract

fetched live from OpenAlex

Standardisation of practice within surgical pathology begins with standardisation of procedures and processes and results in standardisation of practice by all professionals within the surgical pathology department, including pathologists’ assistants (PAs). Healthcare in today’s world is constantly evolving, and surgical pathology is no exception. Testing that was unavailable just years ago can now be considered standard practice that defines a patient’s prognosis and treatment, such is the case with breast cancer biomarkers.1 These tests sometimes emerge into practice faster than their quality control and quality assurance practices can be universally implemented, leading to issues such as those defined by the Cameron Inquiry in Canada2 and the Barne’s Report in the UK.3 These investigations and resulting recommendations call for standardisation of practice in the department directly performing these tests via reproducible, objective, strict quality assurance and control measures, and of all preanalytical and postanalytical steps involved in handling the specimens destined for testing, including standardisation of the professionals involved in these steps. Standardisation in regards to surgical pathology applies to all tasks, beginning with specimen acceptance and accessioning and continues to specimen preparation for fixation, decalcification, ancillary testing, gross descriptions and dissections including specimen sampling, and extends to successive histology techniques such as tissue processing, staining, stain interpretation and immunohistochemistry. Most of these tasks are easily standardised, such as standardisation fixation solutions, fixation times, ischaemic times, programming tissue processors and other automated and easily reproducible tasks. However, standardising a task as interpretive and unique as gross description and dissection of a complex specimen proves more of a challenge. Traditionally, these complex, difficult to standardise functions were performed by the diagnosing pathologist themselves, and their own preferences were sometimes evident in their practice. However, over the span of years, these tasks were slowly undertaken by a new profession that began to …

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)0.027
metaresearch head score (Gemma)0.511
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.483
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0270.511
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0030.003
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 head0.135
GPT teacher head0.544
Teacher spread0.409 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2017
Admission routes2
Has abstractyes

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