Developing and evaluating evidence-based medicine in pediatric orthopaedic surgery
Bibliographic record
Abstract
The purpose of this thesis was to review how a simple clinical pediatric orthopaedic surgeon might be able to create and use different levels of Evidence Based Medicine. Practicing evidence-based medicine involves the assessment of current available literature for its level of evidence, validity, and significance; and subsequently applying results to clinical practice. Much of the literature in pediatric orthopaedic surgery is level IV (case series) or level V (case reports). Despite the lower level, this literature is still important for reporting adverse events and disseminating information of novel treatment techniques. A case report of a novel adverse event is presented: permanent physeal arrest from the use of eight plate for guided growth. Following, a case series aimed to assess if the Fitbone intramedullary lengthening nail could provide successful lengthening with improved rehabilitation and minimal hospital stay, while achieving therapeutic aims of lengthening and correcting mechanical axis. Thirdly, one year of publications in 3 highly respected pediatric orthopaedic journals was reviewed. The use of “numbers needed to treat” as an adjunct to statistical analysis and level of evidence was determined for each article. And finally, a systematic review of the literature looks at the incidence of venous thromboembolism in pediatric orthopaedics. This study began with a stringent, comprehensive protocol that detailed the plan and search strategy. Initially, a meta-analysis was planned, but due to the level of evidence of the articles included, only descriptive statistics could be used. The many setbacks and delays demonstrated the difficulties in producing literature. A case study, case series, review of use of the statistical analysis Number Needed to Treat, and finally, a systematic review were produced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.132 | 0.365 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.027 | 0.015 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".