Bibliographic record
Abstract
BACKGROUND: Lipofibromatous harmartoma (LFH) is a rare tumour that requires early diagnosis and treatment. To alert physicians to the possibility of this tumour, we review the epidemiologic and pathological characteristics, the presentation, diagnosis and treatment of LFH in the upper extremity. METHODS: We obtained data from a MEDLINE search of the English literature from 1966 to June 2003, using the search terms "lipofibromatous," "hamartomas of the nerve," "macrodactyly" and "intraneural lipoma." STUDY SELECTION: Articles that demonstrated institutional experience (epidemiologic, diagnostic and therapeutic, and complications) and those that provided general reviews were selected for inclusion. Of 501 identified articles, 42 fulfilled the review criteria. DATA EXTRACTION: Relevant clinical, diagnostic and therapeutic evaluations were extracted from the literature and synthesized. DATA SYNTHESIS: LFH is distinguished pathologically from other intraneural tumours by fibrofatty infiltration around the nerve fascicles. Typically, patients present either early with macrodactyly or later with a forearm mass lesion or symptoms consistent with a compressive neuropathy of the affected nerve. Although MRI is an important tool in the diagnosis of LFH, the diagnosis should be supported with a tissue biopsy. Treatment for patients with no neurologic deficit involves prophylactic decompression of affected nerves at all points of possible compression. Patients with macrodactyly may benefit from debulking, joint reconstruction or amputation. CONCLUSIONS: LFH differs from other benign nerve tumours with respect to clinical presentation, pathological characteristics, MRI features and management. Because LFH is extensive and inseparable from the nerve fascicles, excision is not recommended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".