Impact of distance on postoperative follow-up in patients of pediatric surgery: a retrospective review
Bibliographic record
Abstract
Objective: Centralization of medical services in Canada has resulted in patients travelling long distances for healthcare, which may compromise their health. We hypothesized that children living farther from a children's hospital were offered and attended fewer follow-up appointments. Methods: We reviewed children less than 17 years of age referred to the general surgery clinic at a tertiary children's hospital during a 2-year period who underwent surgery. Descriptive statistics were performed. Results: We identified 723 patients. The majority were male (61%) with a median age of 7 years (range 18 days to16 years) and were from the major urban center (MUC) (56.3%). The median distance travelled to hospital for MUC patients was 8.9 km (range 0.9-22 km) vs 119.5 km (range 20.3-1950 km) for non-MUC patients. MUC children were offered more follow-up appointments (72.7% vs 60.8%, p<0.05). No significant differences existed in follow-up attendance rates (MUC 88.5% vs non-MUC 89.1%, p=0.84) or postoperative complications (9.8% vs 9.2%, p=0.78). There were no deaths. Conclusions: Patients living farther from a hospital were offered fewer follow-up appointments, but attended an equivalent rate of follow-ups when offered one. Telemedicine and remote follow-up are underused approaches that can permit follow-up appointments while reducing associated travel time and expenses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".