Media coverage of the persistent vegetative state and end-of-life decision-making
Bibliographic record
Abstract
Background: Conflicting perspectives about the diagnosis and prognosis of the persistent vegetative state (PVS) as well as end-of-life (EOL) decision-making were disseminated in the Terri Schiavo case. This study examined print media coverage of these features of the case. Methods: We retrieved print media coverage of the Schiavo case from the LexisNexis Academic database and used content analysis to examine headlines and text of articles describing Schiavo’s neurologic condition, behavioral repertoire, prognosis, and withdrawal of life support. The accuracy of claims about PVS was assessed. Results: Our search yielded 1,141 relevant articles published (1990–2005) in the four most prolific American newspapers for this case. The most frequent headline themes featured legal (31%), EOL (25%), and political (22%) aspects of the case. Of the articles analyzed, 21% reported that Schiavo “might improve” and 7% that she “might recover.” Statements explicitly denying the PVS diagnosis were found in 6% of articles. Explanations of PVS and other chronic disorders of consciousness were rare (≤1%). Most frequently cited descriptions of behaviors were that the patient responds (10%), reacts (9%), is incapacitated (6%), smiles (5%), and laughs (5%). Withdrawal of life support was described as murder in 9% of articles. Conclusions: Media coverage included refutations of the persistent vegetative state (PVS) diagnosis, attributed behaviors inconsistent with PVS, and used charged language to describe end of life decision-making. Strategies are needed to achieve better internal agreement within the professional community and effective communication with patient communities, families, the media, and stakeholders.
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 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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".