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Record W2889497955 · doi:10.14745/ccdr.v40i18a03

Developing a cells, tissue and organ surveillance system

2014· article· en· W2889497955 on OpenAlexafffundvenueabout
Patricia Kenny, C Hyson, Chris Archibald

Bibliographic record

VenueCanada Communicable Disease Report · 2014
Typearticle
Languageen
FieldMedicine
TopicOrgan and Tissue Transplantation Research
Canadian institutionsPublic Health Agency of Canada
FundersHealth CanadaMinistère de la SantéCanadian Blood ServicesPublic Health AgencyPublic Health Agency of Canada
KeywordsNova scotiaTransplantationAgency (philosophy)Organ transplantationTissue bankData collectionMedicineAdverse effectEuropean unionMedical emergencyIntensive care medicineEnvironmental healthBusinessPathologyGeographySurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: An increasing number of cell, tissue and organ transplant procedures take place each year in Canada, including procedures in clinics, physician and dental offices. The Public Health Agency of Canada (the Agency) is leading the development of a Cell, Tissue and Organ Surveillance System (CTOSS). OBJECTIVE: To create timely, useful and relevant national-level transplantation adverse event data by supporting the development and / or enhancement of provincial and territorial data collection systems. METHODS: Minimum data elements and definitions were established for tissues based on definitions established in the European Union and the United States. Data collection on adverse events related to human allograft tissue transplants began in April 2011 at pilot sites in Alberta, Ontario, Quebec, New Brunswick and Nova Scotia. RESULTS: By December 2013, eight tissue transplantation adverse events were reported. Seven involved corneal tissue and one involved cardiovascular tissue. CONCLUSION: A fully developed CTOSS could increase Canadian capacity to improve patient safety. Data collection and analysis could increase the potential for a better understanding of transplantation adverse events, subsequently inform the development of strategies for overall prevention and reduce the severity of such events. The next steps in developing CTOSS will be to establish data elements and definitions for the cell and organ transplant components of the system and increase the number of pilot sites.

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 score0.520
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.016
GPT teacher head0.269
Teacher spread0.254 · 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

Citations0
Published2014
Admission routes4
Has abstractyes

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