USE OF ANALGESICS IN ELDERLY NURSING HOME RESIDENTS
Bibliographic record
Abstract
To the Editor: We have read with interest the article by Wong et al.1 about the use of analgesics for residents in nursing homes in which they demonstrate that patients are either undertreated or treated with analgesics with potential side effects in elderly patients. However, we noted that, even though there is a high prevalence of pain secondary to fractures and musculoskeletal causes in their database, calcitonin, a known treatment for osteoporotic fractures, was not mentioned in the list of patterns of analgesic drug at baseline. Consequently, two questions come to mind. First, was it possible that some of the patients considered to be “undertreated” were actually being treated with calcitonin as the analgesic treatment for their pain associated to either osteoporotic fractures or musculoskeletal causes? Second, is it possible that physicians are not considering calcitonin as a choice for analgesic treatment in these specific situations? Salmon calcitonin provides an effective analgesic effect in several nonmalignant painful conditions that happen frequently in elderly patients, including sympathetic dystrophy syndrome, adhesive capsulitis, osteoporotic vertebral micro fractures, and evident crush fractures.2, 3 Although its use in the treatment of osteoporosis remains controversial,4 several reports have documented its ability to bring rapid and effective relief, due most likely to the release of β endorphin.5 The effect is rapid, and the undesirable effects are infrequent.2 In summary, it would be interesting to know whether calcitonin was included as an analgesic treatment in Dr. Wong's database and whether this analgesic treatment is really known to be a safe and effective choice for specific indications such as osteoporotic fracture and musculoskeletal conditions that occur frequently in the nursing home population.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".