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Record W1986060736 · doi:10.1111/bjh.12880

Lymphoma diagnosis at an academic centre: rate of revision and impact on patient care

2014· article· en· W1986060736 on OpenAlexaff
Joslin M. Bowen, Anamarija M. Perry, Javier A. Laurini, Lynette M. Smith, Kimberly Klinetobe, Martin Bast, Julie M. Vose, Patricia Aoun, Kai Fu, Timothy C. Greiner, Wing C. Chan, Jamés O. Armitage, Dennis D. Weisenburger

Bibliographic record

VenueBritish Journal of Haematology · 2014
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsMedical diagnosisMedicineReferralLymphomaFollicular lymphomaBiopsyPediatricsInternal medicineRadiologyFamily medicine

Abstract

fetched live from OpenAlex

Few studies have examined the value of a mandatory second review of outside pathology material for haematological malignancies. Therefore, we compared diagnoses on biopsies referred to an academic medical centre to determine the rate and therapeutic impact of revised diagnoses resulting from a second review. We reviewed 1010 cases referred for lymphoma during 2009-2010. For each case, referral diagnosis and second review diagnosis were compared. Revised diagnoses were grouped into major and minor discrepancies and all major discrepancies were reviewed by a haematologist to determine the effect the diagnostic change would have on therapy. There was no change in diagnosis in 861 (85·2%) cases. In 149 (14·8%) cases, second review resulted in major diagnostic change, of which 131 (12·9%) would have resulted in a therapeutic change. The highest rates of revision were for follicular, high-grade B-cell, and T-cell lymphomas. We found higher rates of major discrepancy in diagnoses from non-academic centres (15·8%) compared to academic centres (8·5%; P = 0·022), and in excisional biopsies (17·9%) compared to smaller biopsies (9·6%; P = 0·0003). Mandatory review of outside pathology material prior to treatment of patients for lymphoma will identify a significant number of misclassified cases with a major change in therapy.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.553
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.009
GPT teacher head0.278
Teacher spread0.269 · 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

Citations37
Published2014
Admission routes1
Has abstractyes

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