<sup>11</sup>CO bonds made easily for positron emission tomography radiopharmaceuticals
Bibliographic record
Abstract
The positron-emitting radionuclide carbon-11 ((11)C, t1/2 = 20.3 min) possesses the unique potential for radiolabeling of any biological, naturally occurring, or synthetic organic molecule for in vivo positron emission tomography (PET) imaging. Carbon-11 is most often incorporated into small molecules by methylation of alcohol, thiol, amine or carboxylic acid precursors using [(11)C]methyl iodide or [(11)C]methyl triflate (generated from [(11)C]carbon dioxide or [(11)C]methane). Consequently, small molecules that lack an easily substituted (11)C-methyl group are often considered to have non-obvious strategies for radiolabeling and require a more customized approach. [(11)C]Carbon dioxide itself, [(11)C]carbon monoxide, [(11)C]cyanide, and [(11)C]phosgene represent alternative reactants to enable (11)C-carbonylation. Methodologies developed for preparation of (11)C-carbonyl groups have had a tremendous impact on the development of novel PET tracers and provided key tools for clinical research. (11)C-Carbonyl radiopharmaceuticals based on labeled carboxylic acids, amides, carbamates and ureas now account for a substantial number of important imaging agents that have seen translation to higher species and clinical research of previously inaccessible targets, which is a testament to the creativity, utility and practicality of the underlying radiochemistry.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.044 | 0.023 |
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".