The “Molecularly Unstratified” Patient: A Focus for Moral, Psycho-Social and Societal Research
Bibliographic record
Abstract
The biomedical paradigm of personalised precision medicine - identification of specific molecular targets for treatment of an individual patient - offers great potential for treatment of many diseases including cancer. This article provides a critical analysis of the promise, the hype and the pitfalls attending this approach. In particular, we focus on 'molecularly unstratified' patients - those who, for various reasons, are not eligible for a targeted therapy. For these patients, hope-laden therapeutic options are closed down, leaving them left out, and left behind, bobbing untidily about in the wake of technological and scientific 'advance'. This process creates a distinction between groups of patients on the basis of biomarkers and challenges our ability to provide equitable access to care for all patients. In broadening our consideration of these patients to include the research ecosystem that shapes their experience, we hypothesise that the combination of immense promise with significant complexity creates particular individual and organisational challenges for researchers. The novelty and complexity of their research consumes high levels of resource, possibly in parallel with undervaluing other 'low hanging fruit', and may be challenging current regulatory thinking. We outline future research to consider the societal, psycho-social and moral issues relating to 'molecularly unstratified' patients, and the impact of the drive towards personalisation on the research, funding, and regulatory ecosystem.
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 | 0.091 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.135 |
| Scholarly communication | 0.025 | 0.030 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.023 | 0.044 |
| Insufficient payload (model declined to judge) | 0.004 | 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".