Demographic Profile of Spinal Cord Injury Patients Admitted in a Rehabilitation Centre: An Observational Study from Bangladesh
Bibliographic record
Abstract
Background: Spinal cord injury (SCI) is a life threating condition which has a profound impact in the morbidity and mortality. SCI causes lifetime sufferings and mostly occur among the young adults. Not only in Bangladesh but also worldwide, SCI is a devastating and burdensome condition. This research was conducted to see the demographic profile of SCI patients in Bangladesh. Methods and Materials: This is a retrospective analysis where data was collected from medical records of all SCI patients admitted in between January 2012 to December 2014 from Centre for the Rehabilitation of the Paralysed (CRP), Savar. Results: A total of 1172 SCI patients were analyzed. Most of the patients were in their second to third decade of life which consisted 28.8% (n=338). Among total respondents, 86.2% (n=1010) were male and 13.8% (n=162) were female. Most of the participants 61.1% (n=716) were from rural area. The main cause of SCI was fall from height (FFH) {45.8% (n = 537)} followed by the road traffic accidents (RTA) {24.7% (n = 288)}. Overall, 52.3% (n = 613) of participants suffered from traumatic paraplegia while 60.9% (n = 714) had complete lesion. Among the total participants, 30.70% (n = 359) of participants had skeletal level C1-C7 injury. Division-wise distribution shows that FFH is a major cause of SCI {14.84% (n = 174)} followed by the RTA which is a second most common cause in 8.95% (n = 105) of participants in Dhaka division while SCI due to bull attacks and bullet injury are a major cause in Khulna division {1.02% (n = 12)} and Chittagong division respectively. Conclusion: The data is collected from a tertiary level of rehabilitation centre where extensive demographic data was not previously represented. In many developing countries SCI is neglected, poorly managed and deprived from society. In addition, the present study suggests that demographic factors may affect the characteristics of SCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 0.001 |
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".