Epidemiology, clinical features, and histopathology of sarcomas managed in a Haitian cancer clinic.
Bibliographic record
Abstract
e23515 Background: Sarcomas are among the least described cancers diagnosed in Haiti. Suboptimal surgery and the unavailability of radiation therapy make their management challenging. The main objective of this study was to present the epidemiology, clinical features and histopathology of sarcomas in the Haitian setting. Methods: A seven-year retrospective study was conducted in the cancer program of Innovating Health International (IHI). We included all patients with clinical or histological diagnosis of sarcoma enrolled from January 1, 2014 to December 31, 2020. Date of first visit, age, gender, stage, anatomical site, histology, outcome as of December 31, 2020 and date of death were the main variables selected for this chart review. Mortality rate and overall survival were also evaluated. Results: One hundred and twenty-two (122) patients with sarcomas were diagnosed and treated during the study period. Their mean age was 43.3 years [range: 15-88] and the sample was 62.3% women and 37.7% men. 49.2% of the patients were less than 40 years of age. Among the cases of sarcomas, 86.9% were soft tissue sarcomas and 13.1% bone sarcomas. The lower limbs (36.9%), abdomen (14.8%), head and neck (13.1%), upper limbs (12.3%) and breasts (7.4%) were the most common locations of the sarcomas. 81% of abdominal/pelvic sarcoma cases (n=21) were in women. The most common histological types (n=94) were fibrosarcoma (15.2%), liposarcoma (10.9%), dermatofibrosarcoma protuberans (8.7%), malignant histiocytofibroma (6.5%), rhabdomyosarcoma (5.4%) and gastrointestinal stromal tumor (5.4%). 19.6% of the patients had metastatic disease. The mortality rate for the study period was 53.3% and 17.2% of the patients were lost to follow-up. The median overall survival was 7.2 months for the cohort and 18.4 months for the treated patients (n=70). Conclusions: The sarcomas seen in this Haitian medical clinic mainly affect the soft tissues of limbs, abdomen and head and neck. There is a strong predominance of female patients and about half of the patients are aged less than 40. Despite a low metastatic presentation rate, the prognosis is poor, likely reflecting both the aggressiveness of this group of diseases and the disparities of outcomes between high-income and low-and-middle-income countries.
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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 | 0.001 | 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) | 0.002 | 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".