Barriers and Support-System while Considering Hematopoietic Stem Cell Transplant (HSCT): A Qualitative Study of Pre-HSCT Acute Leukemia Patients from a Standalone Transplant Laboratory in India
Bibliographic record
Abstract
Abstract Introduction Hematopoietic stem cell transplant (HSCT) is the definite treatment for acute leukemia but considering HSCT is challenging for the patients. There are many studies that have described the patients’ experience after HSCT but very few studies have reported their experience before going for HSCT and there is no published report in India on patients’ experience before HSCT. Objective We conducted a qualitative study to understand barriers, and support-system while considering HSCT and the chances of getting matched unrelated donor (MUD) for these patients. Materials and Methods The present study was a qualitative study. Demographic details of 514 patients who consented for the study were noted and the patients and their families were interviewed using a semistructured interview booklet before HSCT. The interview sessions were recorded, transcribed verbatim, and analyzed for emerging themes. The study data were analyzed using QDA Miner Lite 4.0 software (Provalis Research, Montreal, Canada). Descriptive statistics such as frequency and percentage were used. The chances of getting a human leukocyte antigen (HLA)-matched donor were also computed by “HLA-matching software.” Results Acute myeloid leukemia (64.01%) was commoner than acute lymphoid leukemia (35.99%) with male: female ratio as 1.98:1. The study showed nine themes as barriers and six themes emerged in regard to the support system for HSCT decision making. The biggest barriers identified among these patients pre-HSCT were related to cost, probability of “success of transplant,” and probable “quality of life.” The family support was the biggest support system variable followed by “treating doctor.” The chances of getting a MUD for these patients were 13.22% and 5.44% in global and Indian data pool, respectively. Conclusion Deciding upon HSCT can be challenging for patients and understanding of barriers and support-system variables among these patients would provide important insights and help design better counseling techniques for such patients of HSCT and future studies in this context.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".