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Alternative Medicine Use in Older Americans

2001· letter· en· W3151380463 on OpenAlexaff
James A. Mertz, Dacbr

Bibliographic record

VenueJournal of the American Geriatrics Society · 2001
Typeletter
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsCanadian Chiropractic Association
Fundersnot available
KeywordsChiropracticMedicineHealth careMedical prescriptionAlternative medicineFamily medicineMEDLINEGerontologyNursing

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.252
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.340
Teacher spread0.284 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations12
Published2001
Admission routes1
Has abstractyes

Explore more

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