Bibliographic record
Abstract
To the Editor: We welcome the contribution by Drs. St. John and Montgomery. Their findings are consistent with ours.1 However, contrary to our findings, when they adjusted for physical function, the association lost significance. In our analyses, we adjusted for activity of daily living (ADL) limitations, which were found to be a significant predictor even while the effect of depressive symptoms remained highly significant. Drs. St. John and Montgomery also used ADLs and included an additional measurement for instrumental activities of daily living (IADLs). It appears that they added both variables into their model concurrently rather than determining the individual effect of each. It is possible that the IADL scale undermined the effect of physical impairment as measured using the ADLs. We adjusted for considerably more variables than Drs. St. John and Montgomery, which also may explain the different results. For example, we adjusted for arthritis and stroke, which may affect function. It may be that the physical function measured in their study was a confounder for these medical conditions Cognition did not significantly affect risk of nursing home admissions in Drs. St. John and Montgomery's model. This is inconsistent with some research.2-4 We believe that this requires more research. As a measure of social support, we included home ownership. Although this is not a comprehensive measure of social support, other research has also shown that those who do not own their own home are far more likely to be admitted to a nursing home4 and that home ownership can serve as a proxy for income. We also adjusted for economic level and marriage, which could possibly explain some differences. Drs. St. John and Montgomery acknowledge differences in the admitting practices for nursing homes in the United States and Canada. In Canada, entering a nursing home requires a panel review. Medicare requires a 3-day prior hospitalization and a physician's note that the individual can show some improvement before reimbursement is approved, which affects many admissions, although for direct admissions to long-term care in a nursing home, U.S. facilities accept anyone based on bed availability and insurance status (i.e., availability of private pay or Medicaid). Therefore, measures of physical function, cognitive status, depression, and physical health may play a different role in the risk of admissions in the United States than in Canada, where residents are only admitted for long-term care. Financial Disclosure: The authors do not have any financial investment in this research. Author Contributions: Dr. Harris performed the analysis with input from Dr. Cooper. Dr. Cooper and Dr. Harris both authored the letter. Sponsor's Role: There was no outside sponsorship of this research.
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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.021 | 0.026 |
| Insufficient payload (model declined to judge) | 0.021 | 0.020 |
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".