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Record W1664448138 · doi:10.26530/oapen_459991

Ted Freeman and the Battle for the Injured Brain: A case history of professional prejudice

2013· book· en· W1664448138 on OpenAlexfundno aff
Peter McCullagh

Bibliographic record

VenueANU Press eBooks · 2013
Typebook
Languageen
FieldMedicine
TopicTraumatic Brain Injury Research
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilMedical Research CouncilRoyal College of Surgeons of EdinburghNew Brunswick Innovation Foundation
KeywordsBattlePrejudice (legal term)PsychologyCriminologyPsychoanalysisSocial psychologyHistoryArchaeology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.099

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0260.016
Scholarly communication0.0050.005
Open science0.0020.006
Research integrity0.0080.014
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.106
GPT teacher head0.333
Teacher spread0.227 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2013
Admission routes1
Has abstractyes

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