Bibliographic record
Abstract
The University of Toronto Lupus Clinic at the Toronto Western Hospital celebrated its 40th anniversary April 2, 2011.Great cause for celebration, particularly, since more than 1600 patients have been registered in what is one of the largest databases of lupus patients in the world -probably the only one following patients prospectively according to a standard protocol since 1970 -it was also an opportunity to pay tribute to Dr. Murray Urowitz, founder and director of the clinic, and to his contributions to lupus care and research.This milestone celebration took the form of a symposium, held April 2, 2011; several current and previous trainees were invited to give oral presentations on their current work as it relates to their training at the Lupus Clinic, while others submitted abstracts for poster presentations. Accomplishments of the Lupus ClinicBy way of introduction, Dafna Gladman reviewed the achievements of the University of Toronto Lupus Clinic since its inception.Murray Urowitz began the Lupus Clinic in 1970 to study the correlation between clinical and serological activity in systemic lupus erythematosus (SLE).A standardized data retrieval form was developed including demographic features, disease-related features, and laboratory tests and therapies.Of note, there was no computer!Despite the lack of computer assistance, several seminal observations were made.These include the bimodal mortality pattern of SLE and the recognition that patients with SLE were at risk for accelerated atherosclerosis 1 .The impact of disease and comorbidities in the first 110 patients registered at the clinic were then highlighted 2 .A new observation of nail lesions in SLE was described 3 .By 1977, it became clear that computerization of the data would greatly facilitate research.During the first phase data were entered on the University of Toronto mainframe computer using punch cards.In 1980 the first desktop computer was introduced; data were entered using SAS Editor.Subsequently, in 1990, information was transferred to an ORACLE database allowing keyboard data entry.Data retrieval forms, which were developed and updated reflecting the ORACLE data entry format, included updates to
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.253 | 0.082 |
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".