Bibliographic record
Abstract
Living with rheumatoid arthritis (RA) for almost 40 years has not been an easy journey. My disease has been severe and difficult to manage; from the beginning there were challenges getting a diagnosis and in finding medications that were effective long term. Thirty years ago, unable to cope with the extreme pain and with 3 children aged 8, 11, and 13 who needed a functioning mother, my doctor prescribed an opioid. This medication gave me back some quality of life but taking opioids is not without significant risks. No one discussed the challenges I would face if and when the time came to stop taking them. With the opioid crisis there has been more pressure from government and medical licensing bodies to implement policies to restrict access for patients prescribed opioids and to encourage tapering. With the change in policy additional funding and resources are needed to help patients through the process but those supports do not exist across Canada.
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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".