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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".