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Record W2795516213 · doi:10.15419/jmri.111

Demographic Profile of Spinal Cord Injury Patients Admitted in a Rehabilitation Centre: An Observational Study from Bangladesh

2018· article· en· W2795516213 on OpenAlexaff
Ziniya Mustary Rahman, Sharmin Alam, Md. Shujayt Goni, Faruq Ahmed, A K M Tawhid, Md. Shahoriar Ahmed

Bibliographic record

VenueJournal of Medical Research and Innovation · 2018
Typearticle
Languageen
FieldMedicine
TopicSpinal Cord Injury Research
Canadian institutionsCanadian Physiotherapy Association
Fundersnot available
KeywordsSpinal cord injuryMedicineParaplegiaRehabilitationObservational studyMedical recordPhysical therapyPediatricsSpinal cordSurgeryInternal medicinePsychiatry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.198
GPT teacher head0.497
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

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