Diagnostic Tools in the Detection of Physical Child Abuse: A Systematic Review
Bibliographic record
Abstract
Child abuse is a critical social issue. The orthopedic surgeon's role is essential in noticing signs and symptoms of physical abuse. For this reason, several authors have proposed scoring systems to identify abuse early on and reduce undiagnosed cases. The aim of this systematic review is to overview the screening tools in the literature. In 2021, three independent authors performed a systematic review of two electronic medical databases using the following inclusion criteria: physical child abuse, questionnaire, survey, score, screening tool and predictive tool. Patients who had experienced sexual abuse or emotional abuse were excluded. The risk of bias evaluation of the articles was performed according to the Newcastle-Ottawa Quality Assessment Scale Cohort Studies. Any evidence-level study reporting clinical data and dealing with a physical child abuse diagnosis tool was considered. A total of 217 articles were found. After reading the full texts and checking the reference lists, n = 12 (71,035 patients) articles were selected. A total of seven screening tools were found. However, only some of the seven diagnostic tools included demonstrated a high rate of sensitivity and specificity. The main limits of the studies were the lack of heterogeneity of evidence and samples and the lack of common assessing tools. Despite the multiplicity of questionnaires aimed at detecting validated child abuse, there was not a single worldwide questionnaire for early diagnosis. A combination of more than one test might increase the validity of the investigation.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".