Improving Patient Follow-Up in Developing Regions
Bibliographic record
Abstract
BACKGROUND: Cleft surgery follow-up in developing regions is challenging. This study evaluated rates, costs, and satisfaction of 2 follow-up programs at the Guwahati Comprehensive Cleft Care Centre (GC4) in Assam, India. METHODS: For this study, 10,582 postoperative visits were analyzed from May 2011 to November 2013. A questionnaire was administered to subsets of follow-up patients at both locations. Costs were calculated. RESULTS: Eighty-five percent of patients had follow-up at GC4, and 15% were seen in the patients' local districts. One hundred ninety-five questionnaires were completed (122 at GC4, 73 in local districts). Patients with local follow-up had fewer accompanying family members (mean, 1.95 vs 0.99; P = 0.00), fewer days off work (mean, 1.84 vs 1.15; P = 0.19), less lost income (Indian rupees 367 vs 143, P = 0.00), and lower direct costs (mean Rs, 911 vs 299; P = 0.00). The financial burden of local follow-up was significantly lower (P = 0.003). No significant differences were seen for convenience, likelihood of attending follow-up, or satisfaction. Follow-ups increased after revising programs from a mean of 139 monthly visits (follow-up to surgery ratio of 0.722) to a mean of 363 visits (ratio of 1.57). The center's mean cost for local follow-up was Rs 303 per patient, whereas the estimated costs would have been Rs 1100 for follow-up at the center. CONCLUSIONS: This study demonstrates potential improvements in costs and outcomes by changing the model of care. Despite significant follow-up challenges, much progress can be achieved through process changes and outreach follow-up programs. The results have important applications across the developing world.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".