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Record W2914141390 · doi:10.1136/jclinpath-2018-205596

Gross and microscopic changes of liver neoplasms and background hepatic structures following neoadjuvant therapy

2019· review· en· W2914141390 on OpenAlexaff
Anjelica Hodgson, Zuhoor Al-Mansouri, Oyedele Adeyi, Sandra E. Fischer

Bibliographic record

VenueJournal of Clinical Pathology · 2019
Typereview
Languageen
FieldMedicine
TopicCholangiocarcinoma and Gallbladder Cancer Studies
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineLiver transplantationNeoadjuvant therapyHepatocellular carcinomaChemotherapyRadiation therapyTransplantationPathologyOncologyRadiologyInternal medicineCancer

Abstract

fetched live from OpenAlex

Liver transplantation is a surgical option with curative intent used in the management of some cases of hepatocellular carcinoma and cholangiocarcinoma (hilar, rarely intrahepatic). A number of different therapeutic modalities including ablative techniques, arterially directed therapies, radiation and chemotherapy are used in the neoadjuvant setting prior to liver transplantation with the goals of preventing tumour progression, decreasing post-transplant recurrence and possibly downstaging patients with tumour burden beyond what is acceptable by current transplant criteria. Pathologists evaluating hepatic explants must be aware of these neoadjuvant therapies and the alterations induced by them in both tumourous and non-tumourous tissue. In this review, we discuss common neoadjuvant therapies used in in this setting, as well as the gross and microscopic changes induced by these presurgical treatments within hepatic neoplasms as well as the background hepatic parenchyma and nearby structures. Select secondary tumours involving the liver which are pretreated will also be discussed. Finally, proper reporting of these changes will be mentioned.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.931
Threshold uncertainty score0.819

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
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 head0.233
GPT teacher head0.471
Teacher spread0.239 · 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 designOther design
Domainnot available
GenreReview

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

Citations1
Published2019
Admission routes1
Has abstractyes

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