Bibliographic record
Abstract
REACH II, as a randomized control trial, provided no evidence that there was an increase in VA or Medicare expenditures for either REACH intervention.After reaching this conclusion, we noted that, for VA beneficiaries, REACH was associated with significantly lower healthcare costs, and this may have been related to the addition of a structured format for addressing the caregiver's role in managing complex care of individuals with Alzheimer's disease and other dementias.In light of the aforementioned concerns about differences in baseline cost in REACH VA, we believe that our speculative language was reasonable and appropriate.Concerning Weeks' comment about multiple comparisons, we transparently noted in our Discussion section that we did not adjust for multiple comparisons.We chose this approach because we did not believe adjustment was warranted for the primary analyses or would have affected between-group comparisons.Finally, Weeks' conclusion that randomized controlled trials provide criterion standard evidence is, in general, true, but understanding how REACH performs in the real world, outside the rarified atmosphere of clinical trials, is critically important.Only through implementation research and observational study can we explore the possibility that REACH may have created synergies between the coordination of guideline-driven care and the integration of a health system to better meet the needs of chronically ill individuals and support their families.
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 | 0.011 | 0.122 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".