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Record W4312129348 · doi:10.3390/curroncol29120783

Abstracts of the Cell Therapy Transplant Canada 2022 Annual Conference

2022· article· en· W4312129348 on OpenAlexaffvenueabout
Stephanie A. Maier, Tobias Berg, Susan Berrigan, Jonathan L. Bramson, Christopher Bredeson, Guy Cantin, Andrew Daly, Gwynivere A Davies, Mahmoud Elsawy, Alejandro Garcia‐Horton, Wilson Lam, Alix Lapworth, Kylie Lepic, Luciana Melo Garcia, Kirk R. Schultz, Ram Vasudevan Nampoothiri, Darrell White, Jean‐Sébastien Delisle

Bibliographic record

VenueCurrent Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicBiomedical Ethics and Regulation
Canadian institutionsHôpital Maisonneuve-RosemontUniversité de MontréalQueen Elizabeth II Health Sciences CentrePrincess Margaret Cancer CentreCentre hospitalier universitaire de QuébecOttawa HospitalHôpital de l'Enfant-JésusMcMaster UniversityBC Children's HospitalDalhousie UniversityHamilton Health Sciences
Fundersnot available
KeywordsMedicineCell therapyCellBiology

Abstract

fetched live from OpenAlex

On behalf of Cell Therapy Transplant Canada (CTTC), we are pleased to present the Abstracts of the CTTC 2022 Annual Conference. The conference was held in-person 15-18 June 2022, in Niagara Falls, Ontario. Poster authors presented their work during a lively and engaging welcome reception on Thursday, 16 June, and oral abstract authors were featured during the oral abstract session in the afternoon on Friday, 17 June 2022. Thirty-three (33) abstracts were selected for presentation as posters and six (6) as oral presentations. The top abstracts in each of four (4) categories, (1) Basic/Translational sciences, (2) Clinical Trials/Observations, (3) Laboratory/Quality, and (4) Pharmacy/Nursing/Other Transplant Support, received awards for both the oral and poster presentations. All of these were marked as "Award Recipient" with the relevant category. We congratulate all the presenters on their research and contribution to the field.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score0.999

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)0.0010.000

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 head0.070
GPT teacher head0.347
Teacher spread0.277 · 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 designNot applicable
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

Citations2
Published2022
Admission routes3
Has abstractyes

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