A Narrative Review of the Rationale for Conducting Neonatal Emergency Studies with a Waived or Deferred Consent Approach
Bibliographic record
Abstract
Emergency research studies are high-stakes studies that are usually performed on the sickest patients, where many patients or guardians have no opportunity to provide full informed consent prior to participation. Many emergency studies self-select healthier patients who can be informed ahead of time about the study process. Unfortunately, results from such participants may not be informative for the future care of sicker patients. This inevitably creates waste and perpetuates uninformed care and continued harm to future patients. The waiver or deferred consent process is an alternative model that may be used to enroll sick patients who are unable to give prospective consent to participate in a study. However, this process generates vastly different stakeholder views which have the potential to create irreversible impediments to research and knowledge. In studies involving newborn infants, consent must be sought from a parent or guardian, and this adds another layer of complexity to already fraught situations if the infant is very sick. In this manuscript, we discuss reasons why consent waiver or deferred consent processes are vital for some types of neonatal research, especially those occurring at and around the time of birth. We provide a framework for conducting neonatal emergency research under consent waiver that will ensure the patient's best interests without compromising ethical, beneficial, and informative knowledge acquisition to improve the future care of sick newborn infants.
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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 | 0.015 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".