Bibliographic record
Abstract
The elderly represent a large subset of the rheumatic population, some of whom have experienced musculoskeletal disease since early life or middle age, whereas others are affected for the first time in their later years. They may be afflicted with a variety of musculoskeletal disorders, some of which occur almost exclusively in elderly individuals. Advancing age may be accompanied by failure of the musculoskeletal system and other major organs. As a result, elderly patients frequently receive concurrent treatment with several pharmacologically active compounds, which increases the potential for significant adverse drug-drug interactions. In addition, the elderly may be less tolerant of certain classes of compounds, including some antirheumatic drugs, necessitating careful drug selection and patient monitoring. Diagnostic and therapeutic decision making may be impeded by the patient's inability to recall completely and accurately important historical details, particularly those relating to drug therapy. Treatment objectives may be compromised further by poor compliance, and adequate follow-up made more difficult by the patient's lack of mobility and declining independence. Successful management of the elderly rheumatic patient, therefore, requires an accurate clinical assessment, comprehensive evaluation of major organ functioning, identification of potential drug-drug interactions, appropriate selection of anti-rheumatic drugs and other treatment modalities, effective doctor-patient communication, and careful monitoring for both beneficial and adverse responses to therapy.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".