The Spectrum Captured: A Methodological Approach to Studying Incidence and Outcomes of Traumatic Brain Injury on a Population Level
Bibliographic record
Abstract
OBJECTIVE: Drawing on the experience of conducting the Brain Injury Incidence and Outcomes New Zealand in the Community study, this article aims to identify the issues arising from the implementation of proposed guidelines for population-based studies of incidence and outcomes in traumatic brain injury (TBI). STUDY DESIGN AND SETTING: All new cases of TBI (all ages and severities) were ascertained over a 1-year period, using overlapping prospective and retrospective sources of case ascertainment in New Zealand. All eligible TBI cases were invited to participate in a comprehensive assessment at baseline and at 1-month follow-up. RESULTS: Our experience to date has revealed the feasibility of case ascertainment methods. Consultation with community health services and professionals resulted in feasible referral pathways to support the identification of TBI cases. 'Hot pursuit' methods of recruitment were essential to ensure complete case ascertainment for this population with few additional cases of TBI identified through cross-checks. CONCLUSION: This review of proposed guidelines in relation to practical study methodology provides a framework for future comparable population-based epidemiological studies of TBI incidence and outcomes in developed countries.
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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 | 0.348 | 0.483 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".