Exercise interventions for health: time to focus on dimensions, delivery, and dollars
Bibliographic record
Abstract
Background The relative frequency of compressive and non-compressive myelopathies and their aetiologies have not been evaluated extensively in most sub-Saharan African countries. The case of Cameroon is studied. Methods Admission registers and case records of patients in the neurology and neurosurgery departments of the study hospital were reviewed from January 1999 to December 2006. Results 224 (9.7% of all admissions) cases were non-traumatic paraplegia/paraparesis or tetraplegia/tetraparesis and 147 were due to myelopathies, representing 6.3% of all cases admitted during the study period and 65.6% of cases of paraplegia or tetraplegia; 88% were compressive myelopathies. Aetiologies were dominated by primary and secondary spinal tumours (mainly prostate carcinoma, lymphoma and liver carcinoma) that each accounted for 24.5% of cases. Other causes included spinal tuberculosis (12.9%), tropical spastic paraparesis (five positive for human T cell lymphotrophic virus (HTLV)-I and one for HTLV-II) (4.8%), spinal degenerative disease (4.1%), acute transverse myelitis (4.1%), HIV myelopathy (1.4%), vitamin B12 deficiency myelopathy and multiple sclerosis (0.7%). No aetiology was found in 21.1% of participants. Conclusions Myelopathies in our setting are dominated by spinal compressions. Metastasis is a leading cause of spinal cord compression with liver carcinoma being more frequent than reported elsewhere. Infections nevertheless remain a major cause of spinal cord disease and both cancers and infections constitute public health targets for reducing the incidence of myelopathies.
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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 | 0.051 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.041 | 0.005 |
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".