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The Internacional Registry in Organ Donation and Transplantation

2012· article· en· W2326102763 on OpenAlexaboutno aff
Maria Gomez, Estephan Arredondo Cordova, Martí Manyalich

Bibliographic record

VenueTransplantation · 2012
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsnot available
Fundersnot available
KeywordsOrgan donationDonationTransplantationMedicineOrgan procurementOrgan transplantationFamily medicineIntensive care medicineSurgeryPolitical scienceLaw

Abstract

fetched live from OpenAlex

Introduction: The International Registry in Organ Donation and Transplantation (IRODaT) has been developed by the Transplant Procurement Management (TPM) since 2001. It provides periodical overview of international data on organ donation and transplantation. In 2010 the registry was transferred to the Donation and Transplantation Institute (DTI foundation) with the aim to improve and innovate its methodology. A new data base is currently being developed to collect the required information on three different levels: National - Regional-Hospital, respectively. Purpose: IRODaT aims at supporting the transplant community by providing up-to-date data on organ donation and transplantation worldwide. Hence, any similarities or differences between countries, regions and/or centers could be identified. Materials and methods: The database, easily to be accessed and managed, it gives, up-to-date information provided by a network of professionals directly involved in the various stages of the donation and transplantation process. The registry provides numbers of Donors after Brain death (DBD), Donors after Cardiac Death (DBD) and Living Donors (LD) as well as specific organ transplantation activities related to the three types of organ donation. Follow-up has been continuously performed. All numbers are constantly checked and updated. New countries are invited to participate in the project and they have already started to report their organ donation activity to the international community. Results: Data on organ donation and transplantation from 65 countries has been collected and compiled for 2009 and 2010. New countries have reported their statistics: Bolivia, Belarus, Canada, Cuba, Nicaragua, Lebanon and Peru. Data analysis revealed a remarkable increase in donation rate in such countries as Croatia, Italy, Slovenia, Czech Republic, Germany, Hungary, Australia, Luxemburg, Poland, Brazil, Singapore, Iran, Saudi Arabia, Venezuela, Romania, Bulgaria, Mexico, Russia and Argentina.[Fig 1][Fig. 2][Fig. 3 & 4]Conclusions: IRODaT is able to provide data and basic statistics with a short timeframe, based on an extensive network of key persons and experts involved in the organ donation and transplantation process worldwide. National and comparative statistics carried out on an international basis can be provided. They have turned out to be of an extreme value to scientific programs, social and governmental bodies. Thanks to these initiatives it is possible to analyze current practices in organ and tissue donation in any country or region of the world.

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)0.010
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.012
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0150.005

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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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 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".

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Citations1
Published2012
Admission routes1
Has abstractyes

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