Spinal Cord Injury in Postearthquake Haiti: Lessons Learned and Future Needs
Bibliographic record
Abstract
Just before 5 PM on January 12, 2010, a 7.0 earthquake struck Haiti, a Caribbean nation that shares the island of Hispaniola with the Dominican Republic.The epicenter was approximately 10 miles southwest of Port-au-Prince, the capital of Haiti.Port-au-Prince is the country's largest city and is an overcrowded, sprawling urban center with a population before the earthquake of approximately 3 million people, including the surrounding areas.The earthquake was devastating and more than twice as lethal as any previous magnitude 7.0 event [1].Precise totals will never be known, but current United Nations estimates are 250,000-300,000 deaths, which is more than twice that of the atomic bomb dropped on Hiroshima at the end of World War II [2].In addition, there were 300,000 injuries and 245,000 buildings were destroyed (Figure 1), which rendered 1.5 million Haitians homeless, most of whom are now living in tent cities [1][2][3].Before the earthquake, Haiti was the poorest nation in the Western Hemisphere and was ranked 149 on the 2009 Human Development Index [4].The existing health care system was tenuous, and the majority of Haitians did not have access to regular health services.Although the response from the international community was swift and massive, first responders were still placed in the unenviable position of having to ration medical care because the sheer magnitude of the tragedy outstripped available resources.Some facilities decided not to treat catastrophic injuries, such as spinal cord injuries (SCI), because of the resource intensive needs of these patients, perceived low survival rates, and "minimal chance of ultimate rehabilitation . .." [5].Nevertheless, despite the challenging circumstances, initial efforts led to the stabilization and survival of many individuals with injuries that would have likely been life-threatening in pre-earthquake Haiti.Before the earthquake, individuals living with severe SCIs were largely nonexistent in Haiti.Although the exact number and survival rate before the earthquake is unknown, the feeling among rehabilitation professionals with long-standing involvement in Haiti is that individual cases were sporadic and that persons with severe injuries, if they survived the initial injury and acute period, typically died within the first 1-2 years.This would be consistent with other developing nations.Under these circumstances, there was little need for expertise in the delivery of care to individuals with SCIs.Likewise, there were no true inpatient rehabilitation beds for individuals with SCI.The events of January 12 led to an unprecedented number of SCIs.A preliminary report by Handicap International estimated that there were more than 100 survivors with SCIs [6].The number is now thought to be closer to 150 despite the fact that most persons with cervical injuries did not survive.The situation was compounded by damage to medical facilities, for example, the General Hospital in Port-au-Prince.An undetermined number of persons with SCIs were transferred to facilities outside Haiti, including the United States.Given that acute care facilities were stretched beyond their capacity, an urgent need arose for patients to be discharged to alternative settings once stabilized.Under these difficult and dire circumstances, 3 organizations established SCI units to address this pressing need: Haiti Hospital Appeal (HHA) in Cap-Haitien (northern Haiti), Project Medishare/Univeristy of Miami in Port-au-Prince (central Haiti), and St. Boniface Hospital in Fond-des-Blancs (southern Haiti).Currently these facilities are caring for approximately 60 individuals with SCI.Two authors (A.S.B.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".