Bibliographic record
Abstract
To the Editor :— We read with great interest the recent article in JAMIA by Hendee, in which he called for the creation of a new NIH institute or center to support biomedical engineering, imaging, and informatics.1 We agree with much of his analysis but wish to offer some additional observations and alternative suggestions for how the informatics community might address the concerns that he has raised. The three cited disciplines clearly do “form the infrastructure on which many of the advances in medical science are built,” and we agree that they “must be nurtured and supported so that they will continue to function as the foundation for the knowledge revolutions of the 21st century.” We question, however, Hendee's claim that “there is no home at the NIH for the basic research that is essential to growth of the intellectual capital of these disciplines” and his characterization of the NIH as composed wholly of disease- or organ-specific research agencies. If one reviews the history of research in bioengineering, imaging, and informatics at the NIH, one will find that two entities have funded a large portion of both the basic and applied work in these fields, neither of which is disease- or organ-specific—the National Center for Research Resources (NRCC, formerly the Division of Research Resources or DRR) …
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".