MétaCan
Menu
← Back to cohort
Record W2739868546 · doi:10.1158/1538-7445.am2017-3952

Abstract 3952: Rapid autopsy programs: A systematic review

2017· review· en· W2739868546 on OpenAlexaff
Brian Li, Neesha C. Dhani, WeiYang Yu, Shawn Khan, Elysia Grose

Bibliographic record

VenueCancer Research · 2017
Typereview
Languageen
FieldMedicine
TopicRadiomics and Machine Learning in Medical Imaging
Canadian institutionsMcMaster UniversityPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineTissue bankTissue DonationTranslational researchCancerMEDLINEIntensive care medicineOncologyPathologyInternal medicineOrgan donationTransplantationBiology

Abstract

fetched live from OpenAlex

Abstract A Rapid Autopsy Program (RAP) is an advanced method of biospecimen procurement whereby autopsies are conducted within 2-6 hours postmortem in order to obtain substantial amounts of high-quality, fresh tissue in support of current and future research. Rapid autopsies were first introduced in the late 1980’s and are still a relatively novel approach, but growing in popularity as a viable alternative to traditional tissue sampling methods for cancer research. In oncology, primary tumour, metastatic, and normal tissue from uninvolved organ sites are sampled and can be subsequently snap frozen in liquid nitrogen or preserved in formalin-fixed paraffin-embedded blocks. Fresh tissue can also be distributed immediately to cancer researchers allowing for analyses incompatible with archival frozen or fixed tissue. Tissue degradation has been found to be minimized in different tissue types by decreasing the postmortem interval – the time between death and tissue preservation. Institutions capable of executing the collection and maintenance of such a repository of biospecimens are a monumental asset to clinicians and researchers who are interested in investigating tumour biology, metastatic disease, and treatment response. In addition to the clear scientific benefits of RAPs, these programs offer a unique opportunity for palliative cancer patients to donate their bodies to aid cancer research. This systematic review was conducted to examine the scope of impact of RAPs thus far on biomedical, translational, and clinical research in the field of oncology, in hopes to inform best practice for collection and use of biospecimen in oncology. Medline, Embase, Cochrane Database of Systematic Reviews, Cochrane CENTRAL, and Ovid MEDLINE will be searched with devised strategies and screened from inception until present. In addition, grey literature will be reviewed. Two independent reviewers will be responsible for study selection and data extraction. The results of the systematic review are pending. Citation Format: Brian Li, Neesha Dhani, WeiYang Yu, Shawn Khan, Elysia Grose. Rapid autopsy programs: A systematic review [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3952. doi:10.1158/1538-7445.AM2017-3952

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.042
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.012
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.042
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0080.006
Bibliometrics0.0120.014
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0020.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0080.000

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 head0.303
GPT teacher head0.567
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueCancer Research→Same topicRadiomics and Machine Learning in Medical Imaging→French-language works237,207→