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Record W1500957253

Pathological reporting of colorectal cancer specimens: a retrospective survey in an academic Canadian pathology department.

2008· article· en· W1500957253 on OpenAlexaffabout
Nancy Chan, Anil Duggal, Michele M. Weir, David K. Driman

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsLondon Health Sciences Centre
Fundersnot available
KeywordsMedicinePathologicalColorectal cancerRetrospective cohort studyPathologyGeneral surgeryCancerFamily medicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To survey and improve the pathological reporting of colorectal cancer (CRC) specimens in a tertiary care pathology department. METHODS: We identified CRC specimens reported in a 6-month period before and after educational sessions and the introduction of a standardized CRC synoptic reporting protocol. Gross and microscopic descriptions were analyzed according to published guidelines for important staging and prognostic features. We then reexamined these parameters for a further 6-month period 15 months later to ensure that the quality of reporting had been maintained. RESULTS: In total, 108 and 166 cases were analyzed before and after standardization, respectively. Many features were reported appropriately, including tumour size, type and grade, depth of invasion, nodal status and proximal and distal margin status. Several underreported features showed significant improvement after standardization, including serosal involvement (reporting increased from 22% to 84%), distance to radial margin (from 14% to 64%), extramural venous invasion (from 18% to 88%), host response (from 19% to 94%) and mean number of nodes retrieved (mean numbers retrieved increased from 11 to 16). The subsequent review 15 months later showed continued long-term improvement in these areas. CONCLUSION: Education and synoptic reporting significantly improved CRC reporting at our centre.

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)0.004
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.217
GPT teacher head0.391
Teacher spread0.174 · 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.

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

Citations23
Published2008
Admission routes2
Has abstractyes

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