Feasibility and Data Quality of the National Spinal Cord Injury Registry of Iran (NSCIR-IR): A Pilot Study.
Bibliographic record
Abstract
BACKGROUND: Spinal cord injury (SCI) is one of the most disabling consequences of trauma with unparalleled economic, social, and personal burden. Any attempt aimed at improving quality of care should be based on comprehensive and reliable data. This pilot investigation studied the feasibility of implementing the National Spinal Cord and Column Injury Registry of Iran (NSCIR-IR) and scrutinized the quality of the registered data. METHODS: From October 2015 to May 2016, over an 8-month period, 65 eligible trauma patients who were admitted to hospitals in three academic centers in mainland Iran were included in this pilot study. Certified registered nurses and neurosurgeons were in charge of data collection, quality verification, and registration. RESULTS: Sixty-five patients with vertebral column fracture dislocations were registered in the study, of whom 14 (21.5%) patients had evidence of SCI. Mechanisms of injury included mechanical falls in 30 patients (46.2%) and motor vehicle accidents in 29 (44.6%). The case identification rate i.e. clinical and radiographic confirmation of spine and SCI, ranged from 10.0% to 88.9% in different registry centers. The completion rate of all data items was 100%, except for five data elements in patients who could not provide clinical information because of their medical status. Consistency i.e. identification of the same elements by all the registrars, was 100% and accuracy of identification of the same pathology ranged from 66.6% to 100%. CONCLUSIONS: Our pilot study showed both the feasibility and acceptable data quality of the NSCIR-IR. However, effective and successful implementation of NSCIR-IR data use requires some modifications such as presence of a dedicated registrar in each center, verification of data by a neurosurgeon, and continuous assessment of patients' neurological status and complications.
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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 | 0.100 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 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".