Determination of visual portfolio for surgeons overseas assessment of surgical needs Nigeria study: Consensus generation through an e-Delphi process
Bibliographic record
Abstract
BACKGROUND: Surgery as a public health priority has received little attention until recently. There is a significant unmeasured and unmet burden of surgical illness in low- and middle-income countries (LMICs). Our aim was to generate a consensus among expert pediatric surgeons practicing in LMICs regarding the spectrum of pediatric surgical conditions that we should look out for in a community-based survey for Surgeons OverSeas Assessment of Surgical Needs Nigeria study. MATERIALS AND METHODS: The Delphi methodology was utilized to identify sets of variables from among a panel of experts. Each variable was scored on a 5-point Likert scale. The experts were provided with an anonymous summary of the results after the first round. A consensus was achieved after two rounds, defined by an improvement in the standard deviation (SD) of scores for a particular variable over that of the previous round. We invited 76 pediatric surgeons through e-mail across Africa but predominantly from Nigeria. RESULTS: Twenty-one pediatric surgeons gave consent to participate through return of mail. Thirteen (62%) answered the first round statements and 8 (38%) the second round. In general, the strength of agreement to all statements of the questionnaire improved between the first and second rounds. Overall consensus, as expressed by the decrease in the mean SD from 0.84 in the first round to 0.68 in the second round, also improved over time. The strength of consensus improved for 23 (74%) of the statements. The strength of consensus decreased for the remaining 8 (26%) of statements. Out of the 31 consensus-generating statements, 16 (51%) scored high agreement, 13 (42%) scored low agreement, and 2 (15%) scored perfect disagreement. CONCLUSION: We have successfully identified the pediatric surgical conditions to be included in any community survey of pediatric surgical need in an LMIC setting.
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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 | 0.086 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".