Bibliographic record
Abstract
To the Editor: Last week I saw a patient for the first time since she started her biologic drug. She was very emotional as she described sitting down at the piano bench and picking out a favorite piece of music. She began to play and it was difficult for her, but she blamed that on lack of practice for more than 5 years. Hearing the piano come alive again, she was overwhelmed with emotion, and that is what she was trying to convey to me. I glanced at her hands and she was “wringing” them and abducting and flexing all her fingers, something she had not been able to do painlessly for a very long time. She was reliving the rebirth of her hands. It is no wonder she was close to tears as she thanked me for my clinical gifts. This brought to mind my mother’s hands, and her own love of classical music. My mother was Helen Catherine Howard, and she died in 1999. She developed rheumatoid … Address correspondence to Dr. C. Dewar, Suite 105, 145 West 15 St., North Vancouver, British Columbia V7M 1R9, Canada. E-mail: c.dewar{at}telus.net
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.023 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 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".