Bibliographic record
Abstract
They say that necessity is the mother of invention. And the delivery of rheumatologic care is in need of mothering. In this month’s Journal , Rostom and colleagues1 describe the use and benefits of an innovative eConsult service, developed to improve access to rheumatologic care in the Champlain region in eastern Ontario, Canada. They categorized the 225 eConsults directed to rheumatology over a span of nearly 4 years, between mid-2011 and early 2015, according to type of question posed and the effect of the interaction on face-to-face referral rates. The group found that referrals for osteoporosis, polyarthralgias, and polyarthritis were the most common, and that consults centered around drug treatment and diagnosis. The strengths cited were limited additional demand placed on a strained system, efficiency of response, referral avoidance, and high user satisfaction. With aging baby boomers, population expansion, migration, and the advent of complex rheumatologic therapies, there is a burgeoning need for rheumatologic care. The problem is one of supply and demand: either there is a shortage of rheumatologists or there are too many potential rheumatology patients. The solution is “simple”: What do we need? More rheumatologists. When do we want them? Now. Let us pause, however, to consider the truly scarce resource here: what we really need is increased rheumatologic acumen, systemwide, that can then be dispensed as rheumatologic assessments. Some clinical issues require multiple assessments, others need fewer. And while rheumatologists are acumen-dense and can provide a large number … Address correspondence to Dr. A. Steiman, Sinai Health System/University Health Network, Rebecca MacDonald Centre for Arthritis and Autoimmune Disease, 60 Murray St., Suite 2-223, Box 10, Toronto, Ontario M5T 3L9, Canada. E-mail: amanda.steiman{at}sinaihealthsystem.ca
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".