Mielopatía cervical degenerativa: una patología cada vez más frecuente y que requiere diagnóstico y manejo precoz
Bibliographic record
Abstract
Degenerative cervical myelopathy (DCM) is the most common cause of spinal cord dysfunction in adults. Its prevalence is increasing as a result of population aging. The diagnosis of DCM is often delayed or overlooked, resulting in secondary neurologic morbidity. The natural course of DCM typically presents as a gradual neurological deterioration, with symptoms ranging from muscle weakness to complete paralysis, with variable degrees of sensory deficits and sphincter dysfunction. Magnetic resonance imaging (MRI) and electrophysiological studies allow the assessment of spinal cord function and its structural damage to determine treatment and clinical outcomes. All patients with signs and symptoms consistent with DCM should be referred to a spine surgeon for assessment and tailored treatment. Those patients with mild DCM can be managed non-operatively but require close monitoring and education about potentially alarming signs and symptoms. Surgery is not currently recommended for asymptomatic patients with evidence of spinal cord compression or cervical spinal stenosis on MRI, but they require a structured follow-up. Patients with moderate or severe DCM require surgical decompression to avoid further progression. The objective of this review is to raise awareness of degenerative cervical myelopathy and its increasing prevalence as well as to aid non-surgical healthcare workers for a timely diagnosis and management of this disabling condition.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".