Utility of the Mean Cumulative Function in the Analysis of Fall Events
Bibliographic record
Abstract
BACKGROUND: Falls are the most common cause of injury among elderly people; half of those people fall recurrently. The objective of these simulation studies was to describe the Mean Cumulative Function (MCF) and to evaluate the utility of the MCF in detecting differences between groups experiencing different patterns of event intensities. METHODS: We specified 250 participants per group with a maximum follow-up time of 365 days. A participant could experience 0, 1, 2, 3, or 4 falls. In the baseline experiment, Groups A and B had an average intensity of 60 and 90 days to the first fall event. These event intensities remained constant for events 2-4. Group C represents a short term "strong" initial impact of the intervention modeled for falls 1 and 2, with an average intensity of one fall per 117 days; however, the intervention wanes to "moderate" for falls 3 and 4 with an average intensity of one fall per 90 days. Group D represents a long-term "strong" impact of the intervention modeled by an average intensity of one fall per 117 days for all subsequent events. RESULTS: The MCF was able to detect differences between groups that had varying intensities of subsequent falls. In Group A, all participants experienced at least one fall, whereas Groups B, C, and D had 4, 9, and 15 participants, respectively, who did not experience any falls. The proportion of participants who had 4 falls declined from 84% to 40% in Groups A and D, respectively. When Group A was compared to Group D, the MCF difference detected the prevention of, on average, one fall per person within 175 days. Discussion. A novel instrument for this field of clinical research--the MCF--allows investigators to compare the average number of falls per participant when the intervention reduces the intensity of subsequent falls.
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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 | 0.029 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".