High-Cost Cancer Treatment Across Borders in Conflict Zones: Experience of Iraqi Patients in Lebanon
Bibliographic record
Abstract
PURPOSE: Conflict-induced cross-border travel for medical treatment is commonly observed in the Middle East. There has been little research conducted on the financial impact this has on patients with cancer or on how cancer centers can adapt their services to meet the needs of this population. This study examines the experience of Iraqi patients seeking care in Lebanon, aiming to understand the social and financial contexts of conflict-related cross-border travel for cancer diagnosis and treatment. PATIENTS AND METHODS: After institutional review board approval, 60 Iraqi patients and caregivers seeking cancer care at a major tertiary referral center in Lebanon were interviewed. RESULTS: Fifty-four respondents (90%) reported high levels of financial distress. Patients relied on the sale of possessions (48%), the sale of homes (30%), and vast networks to raise funds for treatment. Thematic analysis revealed several key drivers for undergoing cross-border treatment, including the conflict-driven exodus of Iraqi oncology specialists; the destruction of hospitals or road blockages; referrals by Iraqi physicians to Lebanese hospitals; the geographic proximity of Lebanon; and the lack of diagnostic equipment, radiotherapy machines, and reliable provision of chemotherapy in Iraqi hospitals. CONCLUSION: As a phenomenon distinct from medical tourism, conflict-related deficiencies in health care at home force patients with limited financial resources to undergo cancer treatment in neighboring countries. We highlight the importance of shared decision making and consider the unique socioeconomic status of this population of patients when planning treatment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".