Spinal Lesions as Clinical Manifestations of Plasma Cell Neoplasia
Bibliographic record
Abstract
(1) Background: Plasma cell neoplasia can be separated into independent subtypes including multiple myeloma (MM) and solitary plasmacytoma of the bone (SBP). The first clinical signs patients present with are skeletal pain, most commonly involving ribs and vertebrae. (2) Methods: Retrospective analysis of 114 patients (38 female, 76 male) receiving spinal surgery from March 2006 until April 2020. Neurological impairments and surgical instability were the criteria for intervention in this cohort. Analysis was based on demographic data, Spinal Instability Neoplastic Score (SINS), location of the lesion, spinal levels of tumor involvement, surgical treatment, histopathological workup, adjuvant therapy, functional outcome, and overall survival (OS). (3) Results: The following surgical procedures were performed: posterior stabilization only in 9 patients, posterior stabilization and decompression without vertebral body replacement in 56 patients, tumor debulking and decompression only in 8 patients, anterior approach in combined approach without vertebral body replacement and without biopsy and/or without kyphoplasty in 33 patients, 3 patients received biopsies only, and 5 patients received kyphoplasty only. The histopathology diagnoses were MM in 94 cases and SBP in 20 cases. Median OS was 72 months (53.4-90.6 months). Preoperative KPSS was 80% (range 40-100%), the postoperative KPSS was 80% (range 50-100%). (4) Conclusions: Surgery for patients with plasma cell neoplasia is beneficial in case of neurological impairment and spinal instability. Moreover, we were able to show that patients with MM and a low number of spinal levels to be supplied have a better prognosis as well as a younger age at the time of the surgical intervention.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".