Advancing Neurosurgery Through Translational Research
Bibliographic record
Abstract
Every year, the number of published research articles increases significantly. However, many potentially useful ideas are lost in this flood of data. Translational research provides a framework through which investigators or laboratories can maximize the likelihood that the product of their research will be adopted in medical practice. There are 2 recognizable models of translation appropriate for the majority of research: investigator driven and industry enabled. Investigator-driven research has more range because it does not have to consider the profit margin of research, but it is a slow process. The industry-enabled model accelerates the translational research process through the power of industry funding but is interested primarily in products with potential for profit. Two cases are examined to illustrate different methods of partnering with industry. IMRIS is a company founded by investigators to distribute intraoperative magnetic resonance imaging technology based on a movable high-field magnet. It took 7 years for IMRIS to make its first sale, but it is now a successful company. With neuroArm, a surgical robot, investigators decided to sell the intellectual property to an established company to ensure successful global commercialization. Translational research advances medicine by creating and distributing effective solutions to contemporary problems.
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 | 0.053 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.024 | 0.011 |
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".