Diagnostic yield and cost-effectiveness of investigations in patients presenting with isolated lower motor neuron signs
Bibliographic record
Abstract
Our objective was to investigate the yield and cost-effectiveness of investigations and therapeutic trials of intravenous immunoglobulin (IVIg) in patients presenting with isolated lower motor neuron (LMN) signs. We performed a retrospective chart review of cases diagnosed between January 2007 and September 2013. Investigation results and their impact on outcome, and outcome of IVIg treatment trials were abstracted. Cost was calculated in Canadian dollars (C$). Fifty-nine of 333 patients presented with isolated LMN signs. The majority of patients (61%) evolved to amyotrophic lateral sclerosis (ALS) within 36 months of presentation, while 37.3% remained with progressive muscular atrophy (PMA) with mean follow-up 29.6 months. Of the 1210 tests performed, 4.9% were abnormal. The diagnosis was changed in only one patient where a muscle biopsy revealed a distal myopathy. Fourteen patients received therapeutic trials of IVIg to rule out an IVIg-responsive inflammatory motor neuropathy with no objective clinical benefit. Total group cost was C$630,484.72 (C$10,686.18/patient). IVIg represented 58.7% of total costs. In conclusion, extensive investigations and treatment trials of IVIg have low yield in the work-up of patients with isolated LMN signs and are not cost-effective when clinical features do not suggest an alternative diagnosis to PMA.
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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 | 0.004 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".