Linking spinal cord injury rehabilitation between the World Wars: The R. Tait McKenzie legacy
Bibliographic record
Abstract
Spinal cord injury (SCI) medicine emerged after World War II due to mass casualties, which required specialized treatment centers. This approach to categorical care, however, was first developed during World War I, led by pioneers R. Tait McKenzie and George Deaver, who demonstrated that soldiers disabled by paralysis could return to society through fitness/mobility, recreational and vocational training. McKenzie, a Canadian and the first professor of physical therapy in the US, influenced Deaver and military physicians in Britain, Canada, and the U.S. with his achievements and publications. Although early mortality from SCI was high, advances in the treatment of skin and bladder complications coupled with rehabilitation developed through lessons learned in World War I, resulted in major changes in survival and quality of life for veterans of World War II in England, US, and Canada. Harry Botterell and Al Jousse, founders of Lyndhurst Lodge, the first SCI center in Canada, adopted Deaver's principles and techniques of rehabilitation and Donald Munro's approach to medical complications. The consequences of failing to organize continuity of care in World War I were recognized both by consumers and physicians. Together with John Counsell, a World War II veteran, they formed the Canadian Paraplegic Association, which "revolutionized" the care of veterans with SCI, as well as civilians, women, and children.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.019 |
| Insufficient payload (model declined to judge) | 0.007 | 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".