Bibliographic record
Abstract
BACKGROUND: Tibial fractures are the most common long bone fracture. The standard of care for the treatment of diaphyseal tibial fractures is an intramedullary nail (IMN). Implant removal is one of the most common procedures in bone and joint surgery, and criteria for implant removal are typically left to the treating surgeon. Currently, no clear criteria exist to guide a surgeon's decision to remove implanted tibial IMNs after healing. METHODS: We undertook a retrospective chart review of a single surgeon's practice from January 1996 to February 2005. We identified patients aged 16-70 years with a tibial fracture treated with an IMN. Patients were followed until fracture union and/or request for IMN removal. The following parameters were recorded: reason for implant removal, age, sex, mechanism of fracture, location of fracture, diameter of IMN, Workers' Compensation Board (WCB) status, activity level, litigation status, insurance involvement, height, weight and body mass index (BMI). RESULTS: Factors influencing the likelihood of removal were sex and litigation. Factors not influencing the likelihood of removal were age, weight, height, BMI, diameter of IMN, patients' level of activity, insurance claim involvement and WCB involvement. Overall, 72.2% of patients had an improvement in their symptoms after IMN removal. CONCLUSION: Sex and litigation are positive predictive factors for patient requests to have tibial IMNs removed after healing.
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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.006 | 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".