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Record W1870682151 · doi:10.2196/ijmr.4301

What Kind of Information About Marginal Donors Is Available Through Sources Other Than Health Care Professionals for Patients on the Waiting List for Organ Transplantation?

2015· article· en· W1870682151 on OpenAlexaffvenue
Sara Kamran, Yvon Calmus, Marie‐Pascale Pomey, G. Vidal-Trécan

Bibliographic record

VenueInteractive Journal of Medical Research · 2015
Typearticle
Languageen
FieldMedicine
TopicOrgan Donation and Transplantation
Canadian institutionsUniversité de Montréal
FundersAstellas Pharma
KeywordsWaiting listHealth professionalsOrgan transplantationTransplantationMedicineHealth careIntensive care medicineNursingFamily medicinePsychologySurgeryPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: The current organ shortage has necessitated expanding the criteria for potential donations to marginal donors (older or sick donors whose organs would have been considered unsuitable before). In France, physicians are not required to provide information to recipients about marginal donors except for hepatitis C or hepatitis B infection and non-heart-beating donations. We hypothesized that patients can be informed about these risks by other information sources than health care professionals, such as websites and patient associations. OBJECTIVE: The objectives of the study were to identify the main health information sources of transplant patients other than health professionals and to evaluate the information provided by websites and associations to patients about the risks of transplantation from marginal donors. METHODS: In this study, the information sources for kidney, liver, heart, and lung patients that had already received transplants or registered on waiting lists were identified by a survey in four transplant centers. Further, the information proposed by French and English language websites and patient associations were evaluated, respectively, by a systematic review of websites and a survey among the presidents of kidney, liver, heart, and lung patient associations. RESULTS: For the first survey, (367/402) 91.3% responses were registered. Apart from health professionals identified as the principal information source (363/367) 98.9%, 19 liver and 28 heart patients searched for information on the websites, while 37 kidney and 42 lung patients were more informed by patients' associations. Our two last surveys showed that information about marginal donors is accessible by websites and (10/34) 30% of associations. All of the 60 Internet documents evaluated on French language and English language websites proposed information about marginal donors. Otherwise, (52/65) 80% of these documents were dedicated to health professionals and contained specialized information, difficult to understand by patients. Certain associations, (20/34) 59%, provided information about the risks of transplantation. There were 45/115 patients considering associations as their main information source that were informed by an association's website. However, only (5/22) 23% of associations communicated the risks of transplantation with patients through their websites. CONCLUSIONS: Currently, patients want to be more informed by other information sources than health professionals, particularly by the websites. Nevertheless, they cannot always trust information proposed by these sources. They need to have their physicians inform them about specialized keywords and present them with reliable information sources. So reliable centers such as universities, transplant centers, and associations should develop the quality and quantity of information proposed to patients on their websites.

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)0.005
metaresearch head score (Gemma)0.051
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 score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.051
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.092
GPT teacher head0.461
Teacher spread0.369 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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