Gold Nanoshells in Cancer Imaging and Therapy: Towards Clinical Application
Bibliographic record
Abstract
Evaluation of: Gobin AM, Lee MH, Halas NJ, James WD, Drezek RA, West JL: Near-infrared resonant nanoshells for combined optical imaging and photothermal cancer therapy. Nano Lett. 7, 1929–1934 (2007) [1]. Although hyperthermia has been a promising method of cancer treatment for decades, traditional means of heating tissues involve invasive catheters or whole-body heating systems. The use of nanoparticles in hyperthermia therapy has been developed over the last 4 years and involves the near-infrared heating of these nanoshells without harming healthy tissues. The paper under evaluation demonstrates the improved optical imaging of tumors with nanoshells and the improved long-term survival of mice treated with these particles and near-infrared irradiation. The implications of this work and important future steps are explored in this evaluation.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".