Percutaneous Muscle Biopsies: Review of 900 Consecutive Cases at London Health Sciences Centre
Bibliographic record
Abstract
OBJECTIVE: In the present study we review our experience with 900 consecutive percutaneous muscle biopsies over the period 1993 to 2007. We examined the advantages and limitations of the procedure, biopsy site preferences, diagnostic range, frequency of diagnoses and quality of histopathology. Demographics, referral patterns and patients' perceptions of the procedure were also assessed. METHODS: Cases were identified through the London Health Sciences Centre Department of Pathology database. Standard biopsy procedures were followed using a manual trocar style instrument. With a neuropathology technologist in attendance at all biopsies, biopsies were oriented in the fresh state and snap frozen. RESULTS: Most referrals for muscle biopsy were from neuromuscular neurologists. The procedure was found to be efficient, well-tolerated and produced high quality specimens in all diagnostic categories. No major complications occurred. Failure to obtain an adequate tissue sample, although uncommon (< 2%), was usually due to marked obesity, edema or muscle wasting. Bleeding at the site was rarely problematic and no wound infections were reported. CONCLUSIONS: Needle muscle biopsies represent an efficient alternative to open biopsies when peripheral nerve sampling is not required and when large tissue samples are not needed for extensive biochemical analyses.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".