Air Crash Investigation: Pattern of Spinal Injuries, Management During the COVID-19 Pandemic, and Outcomes
Bibliographic record
Abstract
BACKGROUND: Spinal injuries following an air crash can be fatal, and recognizing the patients who need immediate attention and early management could save those patients from ending up with lifelong disabilities and other consequences. However, taking appropriate actions in a pandemic situation presents additional challenges. We present our report of air crash victims with spinal injuries, along with their patterns, morphology, management, and outcomes during the COVID-19 pandemic. METHODS: An analysis was performed on the spinal injuries of victims of the Boeing 737 crash landing at the Karipur Airport (Calicut International Airport, Kerala, India) who were treated at a tertiary care referral hospital in August 2020. Details of the initial triage, patterns of injury, morphologies, mechanisms, management principles, and outcomes at 9 months postinjury were recorded and analyzed. RESULTS: Of the 47 patients received at our center, 44 survivors were triaged and 13 patients (29.5%) were identified to have spinal injuries of varying severities. The majority of the injuries were chance fractures at the lumbar level, followed by burst and compression fractures. A total of 6 patients underwent surgery, following all COVID-19 guidelines based on priority. All survivors had positive outcomes with our management. No complications such as secondary infections, worsening of neurological deficits, or implant failures were recorded. CONCLUSION: A high incidence of spinal injuries is seen in air crash victims. Early prioritized surgical management in selected patients provides excellent outcomes. Disaster management during a pandemic situation is a difficult task, where proper planning and execution is necessary to provide optimal results.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".