Financial toxicity associated with conflict-induced cross-border travel for cancer care: Experience of Iraqi patients in Lebanon.
Bibliographic record
Abstract
6562 Background: Conflict-induced cross-border travel for cancer care is commonly observed in the Middle East region. There has been very little research conducted on the impact this has on patients or on how cancer centers can adapt their services to meet the needs of this population. This study examines the experience of Iraqi patients with cancer seeking care in Lebanon at the American University of Beirut Medical Center (AUBMC). The aim of this study was to understand the social and financial context of conflict-related cross-border travel for cancer diagnosis and treatment. Methods: After IRB approval, 60 Iraqi patients and caregivers were recruited and interviewed. For background epidemiological data regarding the population of patients seeking cancer care at AUBMC, the diagnoses of adult patients with Iraqi nationality were reviewed from January 2013 to December 2016. Results: For the retrospective sample, the total number of patients was 1284 with 40% presenting with advanced solid tumors. The prospective data emphasized the difficulties around access to the necessary financial resources for cancer treatment abroad as well as patterns of mobility. Fifty four respondents (90%) reported high levels of financial distress. Patients relied upon the sale of possessions (48%), homes (30%) and vast networks to raise funds for treatment. Thematic analysis revealed several key drivers for undergoing cross-border treatment, including (1) the conflict-driven exodus of Iraqi oncology specialists; (2) destruction of hospitals or road blockages; (3) referrals by Iraqi doctors to Lebanese hospitals (4) geographical proximity of Lebanon and (5) lack of diagnostic equipment, radiotherapy machines, and reliable provision of chemotherapy in Iraqi hospitals. Conclusions: As a phenomenon distinct from medical tourism, conflict-related deficiencies in healthcare at home force patients with limited financial resources to undergo treatments in neighboring countries. We highlight the importance of shared decision-making and taking the unique socioeconomic status of this population of patients into account when planning treatment.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".