Feasibility and diagnostic accuracy of Telephone Administration of an adapted wound heaLing QuestiONnaire for assessment for surgical site infection following abdominal surgery in low and middle-income countries (TALON): protocol for a study within a trial (SWAT)
Bibliographic record
Abstract
BACKGROUND: Surgical site infection is the most common complication of abdominal surgery, with a global impact on patients and health systems. There are no tools to identify wound infection that are validated for use in the global setting. The overall aim of the study described in this protocol is to evaluate the feasibility and validity of a remote, digital pathway for wound assessment after hospital discharge for patients in low- and middle-income countries (LMICs). METHODS: A multi-centre, international, mixed-methods study within a trial, conducted in two stages (TALON-1 and TALON-2). TALON-1 will adapt and translate a universal reporter outcome measurement tool (Bluebelle Wound Healing Questionnaire, WHQ) for use in global surgical research (SWAT store registration: 126) that can be delivered over the telephone. TALON-2 will evaluate a remote wound assessment pathway (including trial retention) and validate the diagnostic accuracy of this adapted WHQ through a prospective cohort study embedded within two global surgery trials. Embedded community engagement and involvement activities will be used to optimise delivery and ensure culturally attuned conduct. TALON-1 and TALON-2 are designed and will be reported in accordance with best practice guidelines for adaptation and validation of outcome measures, and diagnostic test accuracy studies. DISCUSSION: Methods to identify surgical site infection after surgery for patients after hospital discharge have the potential to improve patient safety, trial retention, and research efficiency. TALON represents a large, pragmatic, international study co-designed and delivered with LMIC researchers and patients to address an important research gap in global surgery trial methodology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".