Determining Reasons Affecting the Late Treatment of Congenital Talipes Equinovarus: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Neglected congenital talipes equinovarus (CTEV) is a serious problem commonly found in developing countries. This deformity has fatal impacts, including disabilities and educational limitations. Moreover, cases of neglected CTEV are still frequent in Indonesia as one of the developing countries. Thus, this study aims to investigate the reasons behind the late treatment of CTEV. METHODS: This is a qualitative study conducted throughout September 2019. The subjects are six parents of patients with neglected CTEV who are currently being treated in our hospital. The study was conducted by performing an in-depth interview with the patient’s parents to analyze why they delay checking their child to an orthopaedic surgeon. The results were then grouped into themes. RESULTS: The reasons for the delay were multifactorial. Four subjects had more than one reason why they delayed checking their child to an orthopaedic surgeon. The reason for the delay were socioeconomic factors (3 subjects), medical-related problems (3 subjects), intentionally delayed or refused to seek medical care (2 subjects), and misinformation from the previous physician (3 subjects). CONCLUSIONS: The reasons for the late treatment of CTEV were socioeconomic factors, medical-related problems, intentional factors, and misinformation.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".