Bibliographic record
Abstract
The terms “missed injury” and “delayed diagnosis” have undergone evolution in their academic meaning over the last several decades of trauma care. Missed injury is typically reserved for an unidentified injury for which the opportune moment for intervention has passed. A delayed diagnosis is the term given to injuries not identified on the primary or secondary survey of the initial trauma evaluation. There is obvious overlap in the ways these terms are employed throughout trauma care, and specific institutions may possess their own interpretations. Many emergency medicine texts list a missed injury as one that is discovered after the patient has left the Emergency Department (ED), whether discharged home or admitted. This version of the “missed injury definition” would include possible injuries which were suspected in the ED (not truly “missed”), though not officially found due to appropriate delays in imaging while more acute issues are being resolved in the operating room (OR) or Intensive Care Unit (ICU). The national trauma database of the American College of Surgeons defines missed injury as an “injury-related diagnosis discovered after initial workup is completed and admission diagnosis is determined.” 1 Delayed diagnosis was proposed to describe diagnoses that were not found on primary and secondary survey. The tertiary survey was intended to identify many of these injuries, 2 though some literature still defines injuries found during the tertiary survey as “delayed.” 3 , 4 In any case, the use of a tertiary survey should be employed in all trauma evaluations, as it leads to a reduction in clinically significant initially unidentified injuries. 5 Trauma surgery has also created leveling algorithms based on the mechanism of injury to help activate appropriate resources for trauma patients. Finally, multiple evidence-based decision tools (i.e. Ottawa knee rules, Canadian head computed tomography rules, etc.) exist to help delineate imaging decisions.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.009 |
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".