When the Unknown Is Unknowable: Confronting Diagnostic Uncertainty
Bibliographic record
Abstract
The neonatology literature often refers to medical uncertainty and specifically the challenges of predicting morbidity for extremely premature infants, who can have widely varying outcomes. Less has been written about situations in which diagnoses are simply unknown or unattainable. This case highlights the importance of communication amidst uncertainty from a lack of knowledge about aspects of a patient's condition. Using epidemiologic and clinical reasoning, the authors challenge the assumption that diagnostic uncertainty must necessarily portend prognostic uncertainty. When physicians' quest for a diagnosis becomes burdensome and detrimental to the infant's quality of life, this should be abandoned and replaced by focusing on prognosis. The authors focus on the shift of the physician's role toward one of support, assisting the family in ascribing meaning to the dying experience. By focusing on prognosis and support, communication can proceed with more clarity, understanding, and empathy.
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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 | 0.020 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.016 | 0.022 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".