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Futrell and Mahowald present a useful framework bridging technology-oriented deep learning systems and explanation-oriented linguistic theories. Unfortunately, the target article’s focus on generative text-based Large Language Models (LLMs) fundamentally limits fruitful interactions with linguistics, as many interesting questions on human language fall outside what is captured by written text. We argue that audio-based deep learning models can and should play a crucial role.
In this commentary, we challenge the idea that Language Models (LMs) provide explanatorily adequate models of human language. Findings from language evolution show that both humans and LMs fail to produce human-like language via inductive biases alone; the communicative function of language is crucial. More generally, both experimental and computational work underscore the fact that language is more than just incremental prediction.
Futrell and Mahowald argue language model (LM) success suggests humans may learn language entirely through domain-general statistical mechanisms. However, children differ crucially from LMs in their ability to surpass their input, their language learning trajectory, and the presence of a critical period. Until LMs account for these phenomena, it remains possible that human language acquisition is supported by innate, language-specific learning mechanisms.
Regarding the utility of language models for linguistic research, Futrell and Mahowald advance a crackpot realism, wherein the concerns of a powerful elite are portrayed as “realistic” in a sense which is technocratic and detached from broader human consequences.
The commentary argues the authors employ misdirection and strawmanning to cast others as polarized extremes and themselves as the reasonable centrists. We argue that these patterns of misrepresentation ultimately damage any consensus and middle ground they claim to hope to reach.
Most children 2 years and older with uncomplicated acute otitis media (AOM) are prescribed 10-day antibiotic durations, despite national guidelines recommending antibiotics for 5–7 days. Costs are often cited as a barrier to stewardship efforts. As part of a larger clinical trial including 2 systems and 46 clinics, we developed a low-intensity and a high-intensity intervention aimed at reducing antibiotic duration for AOM and evaluated implementation and sustainability for the interventions.
Methods:
Costs associated with each implementation activity were recorded over time, including material/supply costs (eg, printing) and personnel time costs. Sustainability costs were estimated based on ongoing implementation expenses. For each system, we assessed total intervention, activity-specific, and sustainability costs. Aggregate results were reported as the median across systems.
Results:
The total median implementation costs were $3,606 (range $2,540–$4,672) for the low-intensity intervention and $9,203 (range $7,557–$10,849) for the high-intensity intervention. For the low-intensity intervention, the primary cost driver was electronic health record modifications totaling $2,292 (range $1,615–$2,968). For the high-intensity intervention, the primary cost driver was audit and feedback system activation totaling $5,597 (range $2,885–$8,309). Personnel time accounted for over 90% of costs in both study arms. Sustainability costs were $133/year (range $77–$190) for the low-intensity intervention and $764/year (range $628–$901) for the high-intensity intervention.
Conclusions:
Overall costs were low. The high-intensity intervention resulted in higher costs compared to the low-intensity intervention.
By “linguistics,” the target article means usage-based linguistics, which, we agree, plays very well with language models. But the article summarily dismisses important work on meaning from the now disreputable tradition of generative linguistics. We refute the authors’ arguments from distributional semantics and “green tea,” and highlight the importance of formal compositional semantics for the study of thought.
Over an 18-month collaborative at CNWL, Thames Ward an acute mental health ward at St Charles Hospital in the Royal Borough of Kensington and Chelsea, aimed to reduce high-level observations by 50% and decrease the frequency of continuous observation. Baseline data showed 18 high-level observations per week and use of continuous observation every 2.5 days.
Methods:
With support from an Improvement Coach and workshops, the team tested three PDSA-cycle changes: safety huddles including all staff in observation decisions, prioritising risk assessments over automatic observation for new admissions, and improving patientcommunication. Seven in-depth interviews by an Expert by Experience informed communication practices.
Results:
The project achieved a 50% reduction in high-level observations (18 instances down to 9) sustained from January–September 2025. Continuous observation frequency decreased, with up to 43 days between episodes. Violence and aggression incidents did not increase, and bank staff costs fell to £0 in September 2025.
Conclusion:
This work demonstrates that restrictive practices can be reduced without compromising safety, highlights the importance of challenging entrenched routines, and shows that Expert by Experience involvement is central to meaningful improvement.
1. To evaluate the completeness and quality of admission clerking documentation for psychiatric inpatients on Ward F, Neath Port Talbot Hospital.
2. To identify areas for improvement to ensure compliance with national psychiatry standards.
Methods:
A retrospective audit was conducted on 13 December 2024, reviewing admission clerking proformas for all 23 inpatients on Ward F. Documentation was assessed across key domains including patient identification, presenting complaint, psychiatric and medical history, forensic and substance misuse history, risk assessment, mental state and physical examinations, medication history, investigations, impression, and management plan. Audit standards were derived from the Oxford Textbook of Psychiatry, Core Competencies for a Trainee in Psychiatry, and the Royal College of Psychiatrists (CCQI) Standards for Inpatient Mental Health Services: Admission–First 12 Hours (2022).
Results:
Core components of admission clerking were well documented. All patients had recorded identification, history of presenting complaint, psychiatric history, impression, and management plan. Mental state examinations and risk assessments were completed in 22/23 cases; however, documentation predominantly focused on current risk, with limited recording of past risk (4/23) and previous admissions (8/23). Physical examinations were completed in 18/23 cases, with frequent omissions in neurological, cardiovascular, and anthropometric assessments. Substance misuse history was inconsistently documented, particularly with respect to alcohol intake and illicit drug use. Medication history was recorded in 16/23 cases, with variable documentation of dates, signatures, and electronic prescribing status. Investigations were documented in only 4/23 cases.
Conclusion:
Although core elements of psychiatric admission clerking were generally completed, significant gaps were identified in documentation of physical health assessments, substance misuse history, past risk, investigations, and medication history. A revised admission clerking proforma has been introduced incorporating a structured clerking checklist, detailed substance misuse history (including alcohol units, smoking pack-years, and illicit drug use), and comprehensive physical examination with emphasis on neurological assessment. Re-audit following implementation is recommended to evaluate improvements in documentation quality, compliance with national standards, and patient safety.