Bibliographic record
Abstract
When Dr. Karim Qayumi arrived in Canada in 1983 after fleeing Soviet-occupied Afghanistan with his wife and young son, he and his wife practically lived at the local library for 6 months teaching themselves English. Then he rolled up his sleeves and visited the University of British Columbia. Today Qayumi, a professor of cardiovascular and thoracic surgery at UBC, is prime mover behind the creation of a high-tech Centre of Surgical Excellence at the Vancouver Hospital. He says it will give students “dry lab” experience with new technologies, while at the same time educating residents and boosting BC's rapidly developing telemedicine initiatives. Unlike other North American surgical centres that incorporate anatomy or animal laboratories, the Vancouver centre will link, technologically, to these facilities at the UBC medical school and other sites. A “smart classroom” will be connected to the hospital's trauma unit, emergency department and operating rooms. “I can communicate with my students in the whole province,” he says. “Our emphasis is on high tech to facilitate our educational goals.” These goals include the new problem-based curriculum, which depends on small-group tutoring that requires far more professors than the university can afford. Thus, the high-tech solution. Qayumi also believes the centre will raise the quality of the physicians UBC is producing. “We hope the centre will provide the facilities so that once we teach our students and residents something, they will have a consistent base to come to and practise more and more to sharpen their skills.” With help from his son Tarique, Qayumi has also developed interactive “Cyberpatient” software that allows students to take the history of a virtual patient and carry out an examination and offer treatment, while receiving voice and physical responses, such as facial expressions. The software is being tested in 15 medical schools, and results will be available by the end of 2002. “Nobody has tested the validity of these kinds of programs,” says Qayumi. “We want to compare computer-assisted learning with traditional textbook learning.” Eventually, says Qayumi, technology will supplant animals and “human guinea pigs” in the training of surgeons. For example, pressure-based technology currently under development at BC's Simon Fraser University will let students manipulate a surgical instrument and “feel” tissue, and in the process learn “how much to pull, how much to push, how much to hold things together.” However, he insists that the technology is not supposed to replace live patients but to provide “a better learning environment before they go and touch a patient.” He thinks the software will be useful as early as the second year of undergraduate training. This is all a far cry from the conditions at his beleaguered alma mater, the University of Kabul. Qayumi admits that he “doesn't know how he can be useful” during Afghanistan's rebuilding process. “If somebody can convince me that there is money and resources and people to support me so that I can go and build something, and tell me that I am the right person, I'll go,” he says. “I'll go tomorrow.” — Heather Kent, Vancouver
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".