Bibliographic record
Abstract
In this issue, Liddy et al. [1] report their findings regarding improving access to chronic pain services through the use of telehealth e-consultative services. Dr. Liddy and her colleagues tackled a common problem of access to pain medicine consultative services in North America and found that e-consultative services greatly improved access to patient care in the Ottawa health care system. Using these services, patient information was reviewed in a median response time of less than 2 days, compared with an average wait time of 6 months or greater (up to 2.5 years) with a conventional referral and in-person consultation. Although this is significantly longer than the anticipated wait times in the United States (US), it does mirror the challenges of prompt access to care, particularly for patients in rural settings or those of lower socioeconomic status that rely on state or indigent care programs, which may further limit their access to care. The referring primary care physicians greatly valued the service in >90% of cases, citing benefits provided to both patients and themselves, and including avoidance of unnecessary in-person referrals. Unfortunately the study did not capture the patient-perceived value in the e-consultative service. The format of their intervention, … kevin.vorenkamp{at}gmail.com
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".