#45: Reopening a Successful Stem-cell Transplantation Unit in a Public Children’s Hospital in La Plata, Argentina
Bibliographic record
Abstract
Abstract Background Hematopoietic stem cell transplantation (HSCT) is challenging because of economic, educational, and organizational reasons. Argentina is a middle-income country. HSML is a public pediatric hospital with an HSCT unit halted its services in July 2014. Among the reasons for suspending the services were healthcare providers(HCP) burn out, miscommunication, and tasks breakdown. The HSCT service resumed in December 2014. Objectives The primary objective was to describe and share the experiences in resuming the HSCT service by using a multicomponent strategic plan and interventions. The secondary objective was to review the outcomes. Methods Our strategic plan consisted of four main components: (1) Use of quality improvement tools for quality assessment and management (Six Sigma quality improvement processes, root-cause analysis, process maps, and brain storming); (2) participate in international collaborative networks (St. Jude Global Alliance and PRINCIPAL networks, the BFM-Forum-SCT, Sick-Kids Hospital, Canada); (3) participate in national and international training programs; and (4) improve healthcare delivery and organization processes, including infection care and prevention (develop and standardize all HSCT care delivery procedures). Results and Outcomes Healthcare facilities and services auditing and accreditation: An infection prevention and control program was started. This program managed: air and ventilation system improvement (air-conditioning, HEPA filters, positive pressure) and the creation of 47 standardized operating procedures. The HSCT wards were audited 3 times, and received accreditation (2018) by a national accreditation agency in Argentina. Human resources: 7 new doctors and 4 nurses were hired, the nurse:patient ratio was 1:2. 4 doctors attended a course for SPSS software®management; 6 doctors attended different American Society of Hematology H-Clinical Research Institute meetings. Personnel felt a reduction in the level of burn-out after the implementation of a plan designed by all stakeholders. Family information: 28 families received training for management of immunosuppressed patients. Scientific production: 4 clinical trials were accepted in indexed journals, 23 abstracts were presented in scientific meetings, and 27 educational national and international meetings were attended by personnel. Data management: Creation of a patient database (n = 310 patients), and registration of infection episodes for monthly assessment and monitoring patterns of infections (n = 776 events). Outcomes: During 2015–2019, 73 children were transplanted: autologous 11 (15%), match sibling donor (MSD) 35 (48%) and match unrelated donors (MUD) 27 (37%) Median age was 6 years of age (IQR 2–9), 53% were male, and 60% were acute leukemias. At a mean median time of 12 months (IQR 5–18), the overall survival by Kaplan–Meier for MSD and MUD was similar (62.3 ± 10.7% and 54.2 ± 15.0%, log-rank P = 0.54). The cumulative incidence of treatment-related mortality (log-rank P = 0.31), cumulative incidence of relapse (LR P = 0.99) were comparable as well. Infection rates were 40.5% and 69.6% in MSD vs. MUD (OR 3.36; 95% CI 1.14–9.98; P = 0.038). Conclusion The strategic plan implemented allowed us to offer optimal care to children in a public hospital without further financial cost and more satisfied HCPs. Results are comparable to published literature. Implementation of quality improvement interventions leads to the success of our program. Education and collaboration with national and international networks assisted improvement of the standard of care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".