Market of tissue engineering in Canada from 2011 to 2020
Bibliographic record
Abstract
Tissue engineering aims to produce tissue/organ substitutes to improve upon current treatment approaches, thus providing a permanent solution to damaged tissues/organs. This project aimed to perform a market analysis for understanding and promoting the development and commercialization of tissue engineering in Canada. We searched companies that were established between October 2011 and July 2020 via publicly available information and for these companies, we collected and analyzed the corporate level information, including revenues, and number of employees and founder information. The companies assessed were mainly searched from four different industry segments, i.e., bioprinting, biomaterials, cells and biomaterials, and stem-cells related industry. Our results have demonstrated that there are twenty-five tissue-engineering companies registered in Canada. These companies generated an estimated revenue of USD $67 million in the year 2020, most generated by the tissue engineering and stem-cells related industries. Our results also show that Ontario has the largest number of headquarters of tissue engineering companies among the provinces or territories of Canada. It is expected that the number of new products undergoing clinical trials is increased, based on our results of current clinical trials. Altogether, tissue engineering in Canada has shown a huge growth in the past decade and is forecasted to be an emerging industry in Canada for the years to come.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".