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 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 | 0.040 | 0.239 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".