Decreased Mortality Results in Increased Morbidity
Bibliographic record
Abstract
On January 12, 2010, a devastating earthquake measuring 7.0 on the Richter scale occurred in the small Caribbean nation of Haiti. Much of the instantaneous hu-man and structural destruction that resulted from this massive earthquake was broadcast widely via media sources, and the world immediately responded. Within hours of the event, emergency medical teams joined the large number of nongovernmental or-ganizations (NGOs) already operational in Haiti, and humanitarian aid began to fl ow into the capital of Port-au-Prince by land (through the Dominican Republic), air, and sea. More than 6 months following the event, many of the details remain “preliminary”; however, we know that at least 220,000 people died, making this earthquake one of the largest single-day casualty counts in history.1,2 Even though the number of fatalities is staggering, it is believed that mortality rates would have been higher if the international community had not responded so quickly. Physical therapists from around the world have become part of the global response in Haiti. Although there are far too many individuals and organizations to mention here, collectively they have placed their personal and professional lives on hold in order to contribute to the global humanitarian efforts in this devastated country. These physical therapists are an inspirational group of caring people who have made, are making, and will make important contributions in Haiti. They also have indirectly helped to propel physical therapy into the mainstream of humanitarian aid and relief (Fig. 1). I have been fortunate to be involved as part of Toronto Rehabilitation Institute’s (TRI) humanitarian response in Haiti. The TRI has been working with partners from Healing Hands for Haiti at a spinal cord rehabilitation unit that emerged in the post-earthquake phase. Three main sites in Haiti agreed to admit people with spinal cord injuries following
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".