Adapting Elements of Cleft Care Protocols in Low- and Middle-income Countries During and After COVID-19: A Process-driven Review With Recommendations
Bibliographic record
Abstract
A consortium of global cleft professionals, predominantly from low- and middle-income countries, identified adaptations to cleft care protocols during and after COVID-19 as a priority learning area of need. A multidisciplinary international working group met on a videoconferencing platform in a multi-staged process to make consensus recommendations for adaptations to cleft protocols within resource-constrained settings. Feedback was sought from a roundtable discussion forum and global organizations involved in comprehensive cleft care. Foundational principles were agreed to enable recommendations to be globally relevant and two areas of focus within the specified topic were identified. First the safety aspects of cleft surgery protocols were scrutinized and COVID-19 adaptations, specifically in the pre- and perioperative periods, were highlighted. Second, surgical procedures and cleft care services were prioritized according to their relationship to functional outcomes and time-sensitivity. The surgical procedures assigned the highest priority were emergent interventions for breathing and nutritional requirements and primary palatoplasty. The cleft care services assigned the highest priority were new-born assessments, pediatric support for children with syndromes, management of acute dental or auditory infections and speech pathology intervention. A collaborative, interdisciplinary and international working group delivered consensus recommendations to assist with the provision of cleft care in low- and middle-income countries. At a time of global cleft care delays due to COVID-19, a united approach amongst global cleft care providers will be advantageous to advocate for children born with cleft lip and palate in resource-constrained settings.
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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 | 0.094 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".