EVALUATION OF THE SWEDEAMP DATABASE: FOCUS ON COVERAGE AND AMPUTATION LEVEL RATES
Bibliographic record
Abstract
BACKGROUND: The National Board of Health and Welfare manages several national registers in Sweden. This includes the Swedish National Inpatient Register (IPR), covering all surgical operations, and SwedeAmp, focusing on outcomes after lower limb amputations (LLA). However, coverage rates of amputation levels between these registers have not been externally analyzed. OBJECTIVE: To compare SwedeAmp's coverage with IPR for LLA cases and to assess SwedeAmp's accuracy in capturing LLA data. The goal of this study was also to identify potential discrepancies and establish benchmarks for common amputation levels. METHODOLOGY: Data from both registers, covering the years 2018 to 2023, were compared regarding the amputation levels and patient demographics. The coverage rate of the SwedeAmp register was calculated using SwedeAmp data as the numerator and IPR data as the denominator. FINDINGS: The IPR registry recorded 10,788 LLAs across 21 regions (67 hospitals). The SwedeAmp documented 5,246 LLAs covering 17 regions (36 hospitals), leaving 5,542 amputations unaccounted for, mainly due to regions or hospitals not participating in the SwedeAmp registry and lower registration rates in some areas. Key findings include: Achieving full coverage in SwedeAmp (17 regions) would require registering 9,305 LLAs. Both men and women over 85 years were significantly underrepresented. Thirteen regions in SwedeAmp obtained more than 40% coverage rate. 5 regions had more than 50% rate of above-knee amputations (range: 50.9% - 68.2%). 8 regions reporting more than 50% rate of below-knee amputations (range: 53.1% - 88.9%). Among the 67 hospitals performing LLAs, 36 reported to SwedeAmp. Six of these hospitals performed fewer than 10 LLAs over a six-year period. CONCLUSION: SwedeAmp captured 48.6% of initial LLAs in Sweden, highlighting the need for improved data completeness in LLA records, especially as only 13 regions achieved over 40% Coverage. For hospitals performing regular amputation, the proposed benchmark - coverage of ≥60%, with ≤36.3% for transfemoral amputation (TF), ≤8.4% for knee disarticulations (KD), and ≥55.3% for transtibial amputations (TT) – could serve as a target to enhance consistency and accuracy in reporting. Expanding coverage can improve the register's utility in tracking outcomes, setting national standards, aiding research, and supporting clinical decision-making. Layman's Abstract The Swedish National Board of Health and Welfare manages different health records, including the Swedish National Inpatient Register (IPR), which tracks all surgeries, and SwedeAmp, which focuses on people who have had lower limb amputations. This study looked at how much of SwedeAmp’s data matches the IPR, aimed to identify differences and set standards for common types of amputations. We compared the levels of amputation and patient details between the two records to better understand their coverage. The IPR registered 10,788 lower limb amputations (LLAs) across 21 regions and 67 hospitals, while SwedeAmp reported 5,246 LLAs from 17 regions (36 hospitals), leaving 5,542 amputations unaccounted for. To achieve full coverage in SwedeAmp, 9,305 LLAs would need to be registered, representing 86.3% of all amputations in Sweden. Fewer men over 80 years and women over 85 years were included in the SwedeAmp registry compared to the IPR. Thirteen regions in SwedeAmp had a coverage rate of more than 40%. Five regions reported an above-knee amputation rate of over 50%, while eight regions had a below-knee amputation rate exceeding 50%. Among the 67 hospitals performing LLAs, 36 reported data to SwedeAmp. Six of these hospitals performed fewer than 10 LLAs over a six-year period. For hospitals performing regular amputations, a benchmark of ≥60% coverage, with ≤36.3% for transfemoral, ≤8.4% for knee disarticulations, and ≥55.3% for transtibial amputations, could improve consistency in reporting. Increasing SwedeAmp's participation would strengthen the reliability of national data, supporting better outcome tracking, research, and clinical standards. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/44089/33369 How To Cite: Johannesson A.G, Scheving R, Westlund k.L, Fridriksson T. Evaluation of the SwedeAmp database: Focus on coverage and amputation level rates. Canadian Prosthetics & Orthotics Journal. 2024; Volume 7, Issue 2, No.2. https://doi.org/10.33137/cpoj.v7i2.44089 Corresponding Author: Anton G. Johannesson, PhD, CPOÖssur Clinics EMEA, Stockholm, Sweden.E-Mail: ajohannesson@ossur.comORCID ID: https://orcid.org/0000-0001-8729-458X
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 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".