The International Society for Biological and Environmental Repositories Presents Abstracts from Its Annual Meeting Due North: Aligning Biobanking Practice with Evolving Evidence and Innovation May 9–12, 2017 Toronto, Canada
Bibliographic record
Abstract
Objective: To compare the willingness of Chinese children and their family members toward biospecimen donation.Methods: Inpatient, outpatient, and their family members were interviewed in Shanghai Children's Medical Center from November 2015 to June 2016.Chi-square was used to assess the association between categorical variables by IBM SPSS 22.0.Results: A total of 269 10-17-year-old children and 177 family members participated in this survey with the response rate of 91.96%.Children responders holding out that 10 years old is enough to independently sign a consent were more than those of adults.More adult responders insisted reconsenting when the child is 18 years old than children themselves.As to reasons for donation, majors and minors both chose ''Helpful to similar patient'' more than ''Helpful to my child's or my future therapy,'' and preferred ''Contribution to medicine and human'' more than ''Helpful to my family.''As to the refusal reason, the guardians mainly worried that using samples to illegally make profit and disclosing their personal privacy.With children, the top reasons were pain or uncomfortable in the process of donation and personal privacy disclosed.Conclusions: Although minors and majors had different selections on many details of biobank, they were both motivated by helping other patients or promoting medical science, suggesting the driving effect of altruism.And 10 years old is not the most appropriate age to include children in the decisionmaking process of biobank.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.104 | 0.038 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".