Challenges and pitfalls in diagnosis of Parosteal Osteosarcoma: A clinicopathologic study of 23 cases
Bibliographic record
Abstract
Objective: Parosteal Osteosarcoma (PO) is an uncommon variant of osteosarcoma. Diagnosing PO is important due to itsmalignant nature but the diversity of histologic features makes it challenging by adding a number of soft tissue, bony andcartilaginous lesions into the list of differential diagnosis. Our aim was to study the clinicopathologic and histological features ofPO with emphasis on features helpful in its discrimination from other mimicking lesions. Methods: We reviewed 23 cases of PO diagnosed in our institution between January 2001 and August 2015. Results: Femur was the most commonly involved bone (68.2%) along with other long bones and rib in a single case. Soft tissuecomponent was graded as Grade1 in 9(39%), Grade2 in 8(34.7%) and Grade3 in 4(17.3%) cases. Bony component was seeneither in combination of or exclusively as parallel streams and interconnected trabeculae (mosaic-pattern). Out of 9 cases withcartilage component, 3 showed a cartilage cap. 2(8.6%) cases showed dedifferentiation into osteosarcoma. Conclusion: PO should always be considered in the differential diagnosis of every lesion arising from the bone surface.Knowledge of the variations in histologic features helps to reach the correct diagnosis which should never be made withoutradiological correlation.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".