Information Technology–Assisted Treatment Planning and Performance Assessment for Severe Thalassemia Care in Low- and Middle-Income Countries: Observational Study
Bibliographic record
Abstract
BACKGROUND: Successful models of information and communication technology (ICT) applied to cost-effective delivery of quality care in low- and middle-income countries (LMIC) are an increasing necessity. Severe thalassemia is one of the most common life-threatening noncommunicable diseases of children globally. OBJECTIVE: The aim was to study the impact of ICT on quality of care for severe thalassemia patients in LMIC. METHODS: A total of 1110 patients with severe thalassemia from five centers in India were followed over a 1-year period. The impact of consistent use of a Web-based platform designed to assist comprehensive management of severe thalassemia (ThalCare) on key indicators of quality of care such as minimum (pretransfusion) hemoglobin, serum ferritin, liver size, and spleen size were assessed. RESULTS: Overall improvements in initial hemoglobin, ferritin, and liver and spleen size were significant (P<.001 for each). For four centers, the improvement in mean pretransfusion hemoglobin level was statistically significant (P<.001). Four of five centers achieved reduction in mean ferritin levels, with two displaying a significant drop in ferritin (P=.004 and P<.001). One of the five centers did not record liver and spleen size on palpation, but of the remaining four centers, two witnessed a large drop in liver and spleen size (P<.01), one witnessed moderate drop (P=.05 for liver; P=.03 for spleen size), while the fourth witnessed a moderate increase in liver size (P=.08) and insignificant change in spleen size (P=.12). CONCLUSIONS: Implementation of computer-assisted treatment planning and performance assessment consistently and positively impacted indexes reflecting effective delivery of care to patients suffering from severe thalassemia in LMIC.
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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 | 0.000 | 0.000 |
| 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".