Implementation of a data management program in a pediatric cancer unit in a low income country
Bibliographic record
Abstract
INTRODUCTION: Pediatric cancer units in low-income countries lack data on which to base quality improvement initiatives. We implemented a data management program in the oncology unit of the children's hospital of Tegucigalpa, Honduras, and then we assessed training and supervision of data managers, data accuracy, and completeness as well as obstacles encountered. METHODS: Training included 2 days of off-site hands-on instruction in the use of an online database, daily on-site supervision by physicians, periodic online meetings for education and problem-solving, and continuous e-mail support. RESULTS: Of the 652 patients diagnosed with acute leukemia between July 1995 and June 2005, 150 (23%) had not yet been registered in the database at the time of audit and 65 (10%) had missing medical records. The remaining 437 charts (67%) were reviewed by an external auditor and compared to the data entered previously by the two trained data managers. Protocol information was incomplete in 30% of cases, and the cause of death was inaccurate in 18%. All other data were 99% accurate and 93%-100% complete. Obstacles included a limited medical records system, poor organization of the charts, missing records, inconsistently documented protocol information, data managers who lack a medical background, and slow or unreliable internet connections. CONCLUSION: Data managers can be trained to effectively collect basic pediatric oncology data in a low-income country. Addressing inadequacies in the medical record system while providing specific training in protocol-based care and determination of cause of death for both physicians and data managers will improve data quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".