Development and Assessment of a Textbook for Tissue Engineering Lab Instruction
Bibliographic record
Abstract
Over the past decade, there has been a tremendous increase in the number of biomedical engineering/bioengineering (BME/BE) programs offering lecture courses in tissue engineering (TE), yet very few offer a lab component or separate lab course.Given that engineering is an applied field, the benefits of hands-on lab experience are clear.A new textbook entitled A Laboratory Course in Tissue Engineering will be published by Taylor Francis and CRC Press in summer 2012.The lab manual is appropriate for upper-division undergraduates or graduate students without prior hands-on TE experience, the content and structure are intended to facilitate development of new TE lab courses, and an instructor's manual is available.The experiments within the book are based on both classic TE experiments and modern TE techniques and emphasize the importance of engineering analysis, mathematical modeling, and statistical design of experiments.All of the experiments have been extensively tested and refined to improve the likelihood of successful data collection.Seven representative labs were formally assessed during the fall 2011 academic quarter at the University of California, San Diego.Results from an anonymous survey conducted at the end of the quarter indicate that learning outcomes were achieved and that students found the experiments both enjoyable and challenging.A Laboratory Course in Tissue Engineering provides a convenient source of instructional materials and, to our knowledge, will be the first commercially available lab manual for TE instruction.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".