Venture capital investment in urology, 2011 to mid-2021.
Bibliographic record
Abstract
INTRODUCTION: To characterize venture capital (VC) investments in urology in the past decade that represent promising innovations in early-stage companies. MATERIALS AND METHODS: A retrospective analysis of deals made between VC investors and urologic companies from January 1, 2011, through June 28, 2021, was conducted by using a financial database (PitchBook Platform, PitchBook Data Inc). Data on urologic company and investor names; company information and funding categories (surgical device, therapeutic device, drug discovery/pharmaceutical, and health care technology companies); and deal sizes (in US dollars) and dates were abstracted and aggregated. Descriptive and linear regression analyses were conducted. RESULTS: Urology-related VC funding fluctuated from 2011 through mid-2021, but no substantial change was observed in funding over time. In total, 191 distinct deals were made involving urologic companies, totaling $1.1 billion. The four largest funding categories together accounted for $848 million and comprised therapeutic devices ($373 million), surgical devices ($187 million), drug discovery/pharmaceuticals ($185 million), and health care technology ($102 million). At least $450 million (41% of total investments) was invested in companies developing minimally invasive surgical devices. CONCLUSIONS: Urologic VC investments did not increase in the past decade and were allocated more toward devices than pharmaceuticals or health care technology. Given relative patterns within urology, VC investments may shift toward health care technology and away from pharmaceuticals but remain stable for devices. Further investments in promising technologies may help urologists more effectively manage urologic disease while optimizing outcomes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".