2.21 The level of agreement and nature of disagreements between the detection of suspected concussions in the field and initial physician diagnosis
Bibliographic record
Abstract
Background and Objectives Following the detection of a suspected concussion, diagnosis by a physician is recommended. The objectives were 1) to determine the level of agreement between detection of a suspected concussion and subsequent physician diagnosis, and 2) to assess the risk associated with situations of disagreement. Design and Outcomes This is a pilot sub-study of the SHRed Concussions program and reports on sport-related concussions documented before 28/02/2022 in 13–17 year-old participants. Cohen’s kappa (K) was used to examine agreement between detection and the physician’s diagnosis. When other diagnoses were identified, the nature and potential for medical complications were described. Results Of 166 cases of suspected concussion, 162 concussion diagnoses were confirmed upon initial medical assessment (proportion of agreement 0.976, 95% CI: 0.953–0.999; KAPPA= 0.952). Other diagnoses were identified in 5 cases in which 3 were head or neck injury other than concussion and 2 were concussions with an additional diagnosis. In one case a ‘red flag’ was identified by the physician (increasing headache), leading to brain imaging. Of the 4 cases of disagreement, 3 were considered at low risk of complication and only one presented a higher risk. Conclusion A high level of agreement between detection and physician diagnosis was observed. One situation associated with a higher risk of complication resulted from failure to review the SCAT5 ‘red flags’ in a situation of assessment that was not ‘immediate or on-field’. This may inform adjustments to the SCAT5 terminology and support further research on the efficient use of medical resources.
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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 | 0.113 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".