Feasibility of administrative data for studying complications after hip fracture surgery
Bibliographic record
Abstract
PURPOSE: There is limited information in administrative databases on the occurrence of serious but treatable complications after hip fracture surgery. This study sought to determine the feasibility of identifying the occurrence of serious but treatable complications after hip fracture surgery from discharge abstracts by applying the Agency for Healthcare Research and Quality (AHRQ) Patient Safety Indicator 4 (PSI-4) case-finding tool. METHODS: We obtained Canadian Institute for Health Information discharge abstracts for patients 65 years or older, who were surgically treated for non-pathological first hip fracture between 1 January 2004 and 31 December 2012 in Canada, except for Quebec. We applied specifications of AHRQ Patient Safety Indicators 04, Version 5.0 to identify complications from hip fracture discharge abstracts. RESULTS: Out of 153 613 patients admitted with hip fracture, we identified 12 383 (8.1%) patients with at least one postsurgical complication. From patients with postsurgical complications, we identified 3066 (24.8%) patient admissions to intensive care unit. Overall, 7487 (4.9%) patients developed pneumonia, 1664 (1.1%) developed shock/myocardial infarction, 651 (0.4%) developed sepsis, 1862 (1.1%) developed deep venous thrombosis/pulmonary embolism and 1919 (1.3%) developed gastrointestinal haemorrhage/acute ulcer. CONCLUSIONS: We report that 8.1% of patients developed at least one inhospital complication after hip fracture surgery in Canada between 2004 and 2012. The AHRQ PSI-4 case-finding tool can be considered to identify these serious complications for evaluation of postsurgical care after hip fracture.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".