Evidence map›Paper›PMID 22011068›Full record

ArticleAccountability in research2011

Disclosure of individualized research results: a precautionary approach.

David B Resnik

Abstract read
In one paragraph

Article in Accountability in research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

David B ResnikNational Institute for Environmental Health Sciences, National Institutes of Health, Research Triangle Park, North Carolina, USA. resnikd@niehs.nih.gov

Funding

Ethical, social, and legal issues in environmental health researchZIAES102646 · NIEHS · NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES · PI RESNIK, DAVID · 2009 to 2025
$1.8M
Intramural NIH HHS ZIA ES102646
6 · The paper itself

Abstract

Assessing and managing risks to participants is a central point of contention in the debate about disclosing individualized research results. Those who favor disclosure of only clinically significant results think that disclosing clinically insignificant results is risky and costly, and that harm prevention should take precedence over other ethical considerations. Those who favor giving participants the option of full disclosure regard these risks as insubstantial, and think that obligations to benefit participants and promote their autonomy and right to know outweigh the obligation to prevent harm or financial considerations. The risks of disclosing clinically insignificant research results are currently not quantifiable, due to lack of empirical data. The precautionary principle provides some insight into this debate because it applies to decision-making concerning risks that are plausible but not quantifiable. A precautionary approach would favor full disclosure of individualized results with appropriate safeguards to prevent, minimize, or mitigate risks to participants, such as: validating testing methods; informing participants about their options for receiving tests results and the potential benefits and risks related to receiving results; assessing participants' comfort with handling uncertainty; providing counseling and advice to participants; following-up with individuals who receive tests results; and forming community advisory boards to help investigators deal with issues related to disclosure.

Indexed as

Ethics, ResearchPatient ParticipationBeneficenceConfidentialityDecision MakingHumansInformed ConsentJudgmentRiskTruth Disclosure

Identifiers

PMID22011068
PMCPMC3953618

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.