Ted Freeman and the Battle for the Injured Brain: A case history of professional prejudice
Bibliographic record
Abstract
This book recounts some experiences of young Australians with catastrophic brain injuries, their families and the medical system which they encountered. Whilst most of the events described occurred two to three decades ago they raise questions relevant to contemporary medical practice. The patients whose stories are told were deemed to be ‘unsuitable for rehabilitation’ and their early placement in nursing homes was recommended. In 2013, it is time to acknowledge that the adage of ‘one size fits all’ has no place in rehabilitation in response to severe brain injury. Domiciliary rehabilitation, when practicable, may be optimal with the alternative of slow stream rehabilitation designed to facilitate re-entry into the community. Patients’ families were impelled to undertake heroic carers’ commitments as a reaction to nihilistic medical prognoses. It is time for the Australian health care system to acknowledge those commitments, and the budgetary burden which they lift from the system by providing family members with support to retrieve career opportunities, most notably in education and employment, which have been foregone in caring. Medical attendants repeatedly issued negative prognoses which were often confounded by the patient’s long term progress. Hopefully, those undertaking the acute care of young people with severe brain injury will strive to acquire an open mind and recognise that a prognosis based on a snapshot observation of the patient, without any longer term contact provides a flawed basis for a prognosis. The story of these patients and of Dr Ted Freeman has wider implications.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.016 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.002 | 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".