Bibliographic record
Abstract
Neurophatic pain : diagnostic approach\nDiagnostic approach consisting of a careful history, physical examinitation and special tests. It is possible to clarify the nature of insult to the nervous system. Historycal information provides important clues to diagnosis. Knowledge about the course and the tempo of the disease will narrow diagnostic possibilities. Many neuropathic pain are symptoms of multi system illness. Mc Gill pain questionnaire have been developed to help in this effort. Categorical scales use words to describe the magnitude of the pain. Visual Analogue Scales (VAS). maybe used to assess pain relief or pain intensity. Paediatric Pain Scales (PPS) to draw a picture of pain. Activities of daily living (ADL) scales can be used to assess defisits in functional abilities and to monitor a patient's progress over time. Neuropathic Pain Scale (NPS) has been developed to assess distinct pain qualities associated with neuropathic pain syndromes. The physical examinitation include a complete neurological examination (motor system, somato sensory and autonomic assessment). Electrodiagnostic studies performed to the particular clinical situation. Nerve biopsy has little useful information. Neuropathic pain can be a prominent presenting symptom : trigeminal neuralgia, post herpelic neuralgia, alcoholic neuropathy, phantom limb pain, Fabry's disease, burning feet syndrome, reflexsympathetic dystrophy and diabetic neuropathies.\n\nKey words : neuropathic pain- McGill pain questionaire- neuropathic pain scale- paediatric pain scale \nvisual analogue scale
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.028 |
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".