MétaCan
Menu
Back to cohort
Record W3213308901 · doi:10.1007/s10561-021-09968-y

Canadian demand and access to corneal transplantation: a provincial comparison

2021· article· en· W3213308901 on OpenAlexafffundabout
Christine Humphreys, Kyle Maru, Sonia N. Yeung, Guillermo Rocha, Clara C. Chan

Bibliographic record

VenueCell and Tissue Banking · 2021
Typearticle
Languageen
FieldMedicine
TopicRetinal and Optic Conditions
Canadian institutionsUniversity of ManitobaUniversity of British ColumbiaUniversity of TorontoCanadian Blood ServicesBank of Canada
FundersCanadian Blood Services
KeywordsDonationBenchmarkingEquity (law)Transparency (behavior)TransplantationCorneal transplantationBusinessOrgan donationTransplant surgeryMedicineMarketingPolitical scienceEconomicsEconomic growthSurgery

Abstract

fetched live from OpenAlex

To gather information from stakeholders involved in corneal donation and transplantation to inform discussion at the "National Consensus Forum on Improving Cornea Donation and Transplantation Access in Canada" held in February 2020, survey questions were posed to eye banks, transplanting ophthalmologists and organ donation organizations across Canada to learn more about demand, wait times, and access to tissue for transplant. The survey response rate was one hundred percent (100%) for eye banks and organ donation organizations while 64 percent (64%) of transplant ophthalmologists provided feedback. A number of opportunities for improvement were identified including: demand forecasting; infrastructure and strategies to align supply with demand; data collection and benchmarking of wait times for assessment and transplant to support consistency, equitability and transparency in access; and national collaboration in the development of a data strategy to accurately measure demand and access to cornea transplants in a consistent manner across all provinces to facilitate equity in access nationally.

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)0.002
metaresearch head score (Gemma)0.006
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.582

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.013
Science and technology studies0.0040.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.019
GPT teacher head0.288
Teacher spread0.269 · 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".

Quick stats

Citations10
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCell and Tissue BankingSame topicRetinal and Optic ConditionsFrench-language works237,207