Bibliographic record
Abstract
To the editor: “Alternative Medicine Use in Older Americans,” published in your December 2000 issue, reports on an interesting trend. Senior citizens, in record numbers, are now moving away from the traditional model of disease management medical care to a new model of preventive health care, and chiropractic care plays a key role in that shift. Your study misses the mark, however, by stating that chiropractic care could be “problematic in older patients.” To the contrary, chiropractic care has helped many seniors avoid back surgery and hospital stays. In fact, a 1996 study by Coulter et al. published in Topics in Clinical Chiropractic found that “[older] chiropractic users were less likely to have been hospitalized, less likely to have used a nursing home, more likely to report a better health status, more likely to exercise vigorously, and more likely to be mobile in the community. In addition, they were less likely to use prescription drugs.” Similarly, many other studies have shown that the majority of people who visit doctors of chiropractic are concerned about healthy eating habits, wellness, and overall healthy lifestyles. The American Chiropractic Association agrees that every member of the healthcare team should work together for the benefit of patients of all ages. However, we do not believe medical doctors and other healthcare providers should ask patients questions in such a way that it deters them from seeking chiropractic adjustments—a form of treatment that has been proven to benefit young and old alike.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".