Let’s not forget about injury
Bibliographic record
Abstract
Injury is a deadly epidemic. It is both the leading cause of death for Canadian children, and the number one killer of Canadians <45 years of age (1). It is also costly – the SMARTRISK report from 2009 (based on 2004 data) outlined the annual economic burden of injury in Canada to be approximately $20 billion, higher than both heart disease ($18.5 billion) and cancer ($14.2 billion). Regardless, Canadians only contributed $6.6 million to injury organizations (eg, War Amps), while contributions to heart disease (eg, Heart and Stroke Foundation) and cancer (eg, Canadian Cancer Society) were $178 million and $203.5 million, respectively (2). Additionally, the release of the 2015 “Cost of Injury in Canada Report” stated there are now >15,000 deaths, 230,000 hospitalizations, 3,500,000 emergency department visits and 60,000 permanent disabilities occurring from this preventable disease every year. The economic cost of injury in Canada has increased by 35% since 2004 (3). A significant proportion of these injuries occur in the paediatric age group. The mortality statistics alone are equivalent to the loss of 13 classrooms of children every year, or a child dying every 9 h (3).
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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.020 |
| Insufficient payload (model declined to judge) | 0.040 | 0.024 |
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".