Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OVERVIEW: The presence of multiple concurrent medical conditions (also known as multimorbidity) is now a common phenomenon, hence the importance of its measurement. OBJECTIVE: The purpose of this paper is to review the multimorbidity measures that have been published in the literature to date and that are available for use in future research studies. METHOD: Two main groups of measures of multimorbidity could be distinguished. The first group of measures is constituted by a simple count from various lists of chronic conditions. The second group of measures introduces a weighting for included chronic conditions thus creating a "weighted index" of multimorbidity. These groups are not mutually exclusive as the list of medical conditions in some weighted indices can be used as a list of conditions without weighting. This article includes a review of the multimorbidity literature to date that has reported these groups of measurements, showing the variety of existing measurements and highlighting their differences to provide an overview of the possibilities that are available to a researcher intending to measure multimorbidity. CONCLUSION: Finally, we outline some guidelines for the choice of a measurement of multimorbidity for research studies. We hope that this review of the existing literature will help inform the careful use of these tools by researchers moving forward. In addition to this review, it is advised that readers attempt to keep updated on the ever-increasing multimorbidity literature. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it