Should Recommendations for Clinical use be Restricted to Patients who were Enrolled in a Pivotal Clinical Trial?
Bibliographic record
Abstract
This article refers to 'What proportion of patients with chronic heart failure are eligible for sacubitril-valsartan?' by P. Pellicori et al., published in this issue on pages 768-778.A large-scale clinical trial in patients with chronic heart failure and a reduced ejection fraction demonstrates that a new treatment reduces the risk for cardiovascular death.The results are clinically meaningful and statistically persuasive.The drug is approved for use throughout the world.How should we select patients to receive the drug in clinical practice?Many would say that the drug should be prescribed to patients who were similar to and treated similarly to those enrolled in the pivotal trial that demonstrated its benefits.Yet, doing so precisely would represent a practical impossibility.The trial was carried out in specialized centres by research teams, who were expert in the care of patients with heart failure.The protocol specified dozens of entry criteria; many were applied only to make the trial efficient rather than to focus specifically on patients who might benefit.Patients were provided with free study medication and were followed closely by attentive study coordinators.After commercialization, should we require that all these conditions be duplicated for patients to receive treatment?We do not demand that practitioners act as clinical trialists when they manage patients.We do not mandate that treated patients be identical to those studied or be cared for under highly supervised conditions.If we did so, most patients who would benefit from the new life-prolonging drug would never receive it; the findings of the clinical trial would never be translated for the public good.
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 | 0.387 | 0.684 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.069 | 0.062 |
| Insufficient payload (model declined to judge) | 0.024 | 0.035 |
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".