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
PURPOSE: Injuries due to falls are an important public health concern, particularly for the elderly, and effective prevention is an ongoing endeavour. The present study has two related objectives: (1) to describe associations between drug use and falls in an institutionalized population, and (2) to identify a high risk subgroup within the larger population. METHODS: The initial analysis was based on a population of 227 residents who were followed over a 1-year period. Logistic regression techniques were used to estimate odds ratios (ORs) of the association of falls and drug use. The study of potential 'high-risk' groups employed a case-crossover design to estimate the risk of falling associated with starting a new drug course. RESULTS: Relatively weak ORs for risk of falling were observed for various drug classes; the highest OR was for benzodiazepines (BZD) at OR = 1.8 (unadjusted). Residents taking multiple drugs were at particular risk for falling, e.g. an OR of 6.1 for those using 10+ drugs. The case-crossover analysis indicated that residents starting a new BZD/antipsychotic were at very high risk (OR = 11.4) for experiencing a fall. CONCLUSIONS: Residents who took many different types of medications, as well as residents starting a new BZD/antipsychotics were at greatly increased risk of falling. These are high risk groups where increased monitoring or adjustments to drug regimens could lead to prevention of falls.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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