Hospital separations for injuries due to unintentional falls [<sup>1</sup>], British Columbia, 1991–2010 [<sup>2</sup>].
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
<p>Notes:</p><p>1. "Injury due to unintentional fall" defined as hospital separation with Most Responsible Diagnosis in the range ICD9:800–999 or ICD10:S00-T98, and supplemental diagnosis in the range ICD9:E880-E888 or ICD10:W00-W19.</p><p>2. Injuries occurring during the observation period 1991-Apr-01 to 2010-Mar-31.</p><p>3. Person-years is the sum of the annual population counts times the fraction of each year included in the observation period.</p><p>4. Observed number of injuries.</p><p>5. Expected number, indirectly standardized, based on age, gender and HSDA-specific rates in the total population of BC.</p><p>6. Crude Rate per 10,000 person-years.</p><p>7. Standardized Relative Risk (compared to the total population of BC) = Observed/Expected.</p><p>Hospital separations for injuries due to unintentional falls [<sup><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121694#t001fn002" target="_blank">1</a></sup>], British Columbia, 1991–2010 [<sup><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0121694#t001fn003" target="_blank">2</a></sup>].</p>
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.077 | 0.007 |
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