Proceduralist criteria for evaluating interface utility of novel imaging modalities in early phase clinical trials: evaluating the need for standardized criteria
Bibliographic record
Abstract
Accelerating innovation in the space of fluorescence imaging for surgical applications has increased interest in safely and expediently advancing these technologies to clinic through Food and Drug Administration-(FDA-) compliant trials. Conventional metrics for early phase trials include drug safety, tolerability, dosing, and pharmacokinetics. Most procedural imaging technologies rely on administration of an exogenous fluorophore and concurrent use of an imaging system; both of which must receive FDA approval to proceed to clinic. Because fluorophores are classified as medical imaging agents, criteria for establishing dose are different, and arguably more complicated, than therapeutic drugs. Since no therapeutic effect is desired, medical imaging agents are ideally administered at the lowest dose that achieves adequate target differentiation. Because procedural imaging modalities are intended to enhance and/or ease proceduralists' identification or assessment of tissues, beneficial effects of these technologies may manifest in the form of qualitative endpoints such as: 1) confidence; 2) decision-making; and 3) satisfaction with the specified procedure. Due to the rapid expansion of medical imaging technologies, we believe that our field requires standardized criteria to evaluate existing and emerging technologies objectively so that both quantitative and qualitative aspects of their use may be measured and useful comparisons to assess their relative value may occur. Here, we present a 15-item consensus-based survey instrument to assess the utility of novel imaging technologies from the proceduralist's standpoint.
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 | 0.306 | 0.439 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".